# coding=utf-8 import uiautomator2 as u2 import time import asyncio import logging import sys import os import cv2 import numpy as np import re # 添加项目根目录到 sys.path 以便导入 Util project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) if project_root not in sys.path: sys.path.append(project_root) import json from datetime import datetime from Util.EasyOcrKit import EasyOcrKit # 初始化 EasyOcrKit ocr_kit = EasyOcrKit() # 配置日志 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger("WxUtil") # 目录配置 BASE_DATA_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) LOG_DIR = os.path.join(BASE_DATA_DIR, "Logs") OUTPUT_DIR = os.path.join(BASE_DATA_DIR, "Output") TEMPLATE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Templates") # 全局调试图片计数器 _debug_counter = 0 def get_next_debug_path(desc="step"): """获取下一个顺序命名的调试图片路径 (debug_N_desc.jpg)""" global _debug_counter _debug_counter += 1 filename = f"debug_{_debug_counter}_{desc}.jpg" return os.path.join(OUTPUT_DIR, filename) def clear_directory(dir_path, exclude_files=None): """清理指定目录下的所有文件，支持排除特定文件""" if not os.path.exists(dir_path): os.makedirs(dir_path) return if exclude_files is None: exclude_files = [] import shutil for filename in os.listdir(dir_path): if filename in exclude_files: continue file_path = os.path.join(dir_path, filename) try: if os.path.isfile(file_path) or os.path.islink(file_path): os.unlink(file_path) elif os.path.isdir(file_path): shutil.rmtree(file_path) except Exception as e: logger.warning(f"Failed to delete {file_path}. Reason: {e}") def setup_script_environment(): """运行前清理日志和输出目录""" logger.info("清理运行环境: Logs 和 Output 目录...") # 重置调试计数器 global _debug_counter _debug_counter = 0 # 排除当前正在使用的日志文件 clear_directory(LOG_DIR, exclude_files=["T2_ChatMonitor.log", "WxUtil.log"]) clear_directory(OUTPUT_DIR) def connect_device(): """ 连接设备并返回设备对象，同时打印详细的设备信息 """ try: d = u2.connect() # 强制检查连接是否可用 if not d.info: logger.error("设备连接不可用 (d.info is empty)") return None # 获取可靠的序列号 device_serial = d.serial if hasattr(d, 'serial') else "未知" logger.info(f"设备连接成功: {device_serial}") # 获取并打印详细设备信息 device_info = d.device_info logger.info(f"详细设备信息: 品牌={device_info.get('brand')}, 型号={device_info.get('model')}, SDK={device_info.get('sdk')}") return d except Exception as e: logger.error(f"设备连接失败: {e}") return None def safe_device_click(d, x, y): """ 安全的点击操作，包含简单的异常捕获和重试逻辑 """ try: d.click(x, y) return True except Exception as e: logger.warning(f"点击操作失败 ({x}, {y}): {e}，尝试重新连接并重试...") try: # 尝试重新初始化连接 new_d = u2.connect() new_d.click(x, y) return True except Exception as e2: logger.error(f"重试点击操作依然失败: {e2}") return False def draw_debug_info(image_path, messages, current_voice_center=None, suffix=""): """ 辅助函数：在截图中绘制当前已知的消息状态 :param image_path: 图片路径 :param messages: 消息列表 :param current_voice_center: 当前正在处理的语音中心坐标 (vx, vy) :param suffix: 保存文件名的后缀 """ try: img = cv2.imread(image_path) if img is None: return for msg in messages: if msg['type'] == 'voice': ax, ay = msg['center'] is_unread = msg.get('is_unread', False) is_converted = msg.get('is_converted', False) # 绘制框 color = (0, 0, 255) if is_unread else (0, 255, 0) cv2.rectangle(img, (ax-30, ay-30), (ax+30, ay+30), color, 2) # 绘制 YES/NO label = "YES" if is_converted else "NO" cv2.putText(img, label, (ax + 40, ay + 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2) # 如果是当前正在处理的语音，画一个额外的黄圈 if current_voice_center and abs(ax - current_voice_center[0]) < 10 and abs(ay - current_voice_center[1]) < 10: cv2.circle(img, (ax, ay), 40, (0, 255, 255), 3) cv2.putText(img, "PROCESSING", (ax - 60, ay - 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2) # 保存覆盖后的图片 cv2.imwrite(image_path, img) logger.info(f"已更新调试标记到截图: {image_path}") except Exception as e: logger.warning(f"绘制调试信息失败: {e}") 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） 返回: (messages_list, debug_image) """ img = cv2.imread(image_path) if img is None: logger.error(f"无法读取图片: {image_path}") return [], None h, w = img.shape[:2] # 3. 模板匹配寻找语音图标和红点 audio_template = os.path.join(TEMPLATE_DIR, "audio.jpg") red_point_template = os.path.join(TEMPLATE_DIR, "red_point.jpg") audio_matches = find_all_template_matches(image_path, audio_template, threshold=0.8) red_points = find_all_template_matches(image_path, red_point_template, threshold=0.8) # 4. OCR 识别所有文本 logger.info("正在执行 OCR 识别...") ocr_results = ocr_kit.read_text(image_path) # 4.5 尝试提取聊天标题 (对方昵称) chat_title = "对方" potential_titles = [] for bbox, text, conf in ocr_results: c_y = int((bbox[0][1] + bbox[2][1]) / 2) c_x = int((bbox[0][0] + bbox[2][0]) / 2) # 标题区域通常在顶部 (状态栏下方，消息列表上方) if 60 < c_y < 140: clean = text.strip() # 排除时间、信号、返回按钮等 if re.match(r'^\d{1,2}:\d{2}$', clean): continue if "微信" in clean or "WeChat" in clean: continue if clean in ["<", "返回", "消息", "(", ")"]: continue if re.match(r'^\d+$', clean): continue # 排除纯数字(如未读数) if len(clean) > 0: potential_titles.append((c_x, clean)) if potential_titles: # 优先取最接近水平中心的文本作为标题 potential_titles.sort(key=lambda x: abs(x[0] - w/2)) chat_title = potential_titles[0][1] # 去除可能包含的括号(比如备注名后的群聊人数，虽然后面会被截断) chat_title = re.sub(r'\(\d+\)$', '', chat_title).strip() logger.info(f"识别到聊天标题/对方昵称: {chat_title}") # 微信菜单关键字（用于排除干扰） MENU_KEYWORDS = ["听筒播放", "收藏", "背景播放", "删除", "多选", "取消转文字", "转文字", "引用", "提醒"] # 忽略的系统消息内容 IGNORE_CONTENT = ["撤回了一条消息", "打招呼的消息", "拍了拍", "你撤回了一条消息", "引用", "Clear Text", "Switch IME", "Done"] # 5. 整合所有消息 messages = [] debug_img = img.copy() # 初始化调试图 # 绘制过滤区域边界 (可视化) cv2.line(debug_img, (0, 150), (w, 150), (255, 0, 255), 2) # 顶部线 cv2.line(debug_img, (0, h - 100), (w, h - 100), (255, 0, 255), 2) # 底部线 cv2.putText(debug_img, "TOP_FILTER", (10, 140), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 255), 1) cv2.putText(debug_img, "BOTTOM_FILTER", (10, h - 110), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 255), 1) claimed_ocr_indices = set() # A. 添加语音消息 for ax, ay in audio_matches: # 标记所有找到的语音图标 (用于调试) cv2.circle(debug_img, (ax, ay), 10, (255, 255, 0), -1) # 过滤掉顶部和底部的非聊天区域 if ay < 150 or ay > h - 100: logger.info(f"忽略区域外语音图标: ({ax}, {ay})") cv2.rectangle(debug_img, (ax-35, ay-35), (ax+35, ay+35), (128, 128, 128), 1) cv2.putText(debug_img, "FILTERED", (ax - 40, ay - 45), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (128, 128, 128), 1) continue sender = "对方" if ax < w / 2 else "我" is_unread = False for rx, ry in red_points: # 红点通常在语音图标右侧且 Y 轴相近 if abs(ry - ay) < 50 and rx > ax: is_unread = True break # 改进：判断是否已转文字 is_converted = False converted_trigger_text = "" associated_texts = [] # 存储关联的多行文本 [(y, x, text)] for i, (bbox, text, conf) in enumerate(ocr_results): if i in claimed_ocr_indices: continue c_x = int((bbox[0][0] + bbox[2][0]) / 2) c_y = int((bbox[0][1] + bbox[2][1]) / 2) # 判定逻辑：文本在语音下方且水平偏移不大 (放宽 Y 轴限制以包含侧边的时长文本) # 2025-01-26: 增加 X 轴范围到 900 以适配超长语音条的右侧时长/文本 # 增加 Y 轴范围到 800 以适配多行转文字内容 if -50 < c_y - ay < 800 and abs(c_x - ax) < 900: # 检查中间是否有其他语音图标 has_intermediate_audio = False for other_ax, other_ay in audio_matches: if ay + 20 < other_ay < c_y - 10: has_intermediate_audio = True logger.info(f"语音({ax},{ay}) 被中间语音图标({other_ax},{other_ay}) 阻断，无法关联文本 '{text[:10]}...'") break if has_intermediate_audio: continue 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) # 判定是否为纯数字或时长 is_duration = re.search(r'\d{1,2}"?$', clean_text) and len(clean_text) < 6 # 判定是否为系统消息 is_ignored = any(k in clean_text for k in IGNORE_CONTENT) # 噪音判定 (例如 "少3"") is_noise = "少" in clean_text and len(clean_text) < 8 and re.search(r'\d', clean_text) if not is_duration and not is_timestamp and clean_text not in MENU_KEYWORDS and not is_ignored and not is_noise: is_converted = True associated_texts.append((c_y, c_x, clean_text)) claimed_ocr_indices.add(i) # 不再 break，继续寻找后续文本行 else: # 这些文本虽然不作为内容，但它们属于语音消息的附属信息，标记为已处理 claimed_ocr_indices.add(i) if is_timestamp: logger.info(f"语音({ax},{ay}) 忽略下方时间戳文本: '{clean_text}'") elif is_duration: logger.info(f"语音({ax},{ay}) 忽略时长文本: '{clean_text}'") elif is_noise: logger.info(f"语音({ax},{ay}) 忽略噪音文本: '{clean_text}'") elif is_ignored: logger.info(f"语音({ax},{ay}) 忽略系统消息文本: '{clean_text}'") else: logger.info(f"语音({ax},{ay}) 忽略其他文本(可能是菜单): '{clean_text}'") # 整合所有关联文本 if associated_texts: # 按 Y 轴排序，如果 Y 接近则按 X 轴排序 associated_texts.sort(key=lambda x: (x[0], x[1])) converted_trigger_text = "".join([t[2] for t in associated_texts]) logger.info(f"语音({ax},{ay}) 判定为已转换，最终合并文本: '{converted_trigger_text}'") if is_converted: logger.info(f"语音消息 ({ax}, {ay}) 已有转换文字: '{converted_trigger_text}'，跳过") # 绘图反馈 color = (0, 0, 255) if is_unread else (0, 255, 0) cv2.rectangle(debug_img, (ax-30, ay-30), (ax+30, ay+30), color, 2) label = "YES" if is_converted else "NO" cv2.putText(debug_img, label, (ax + 40, ay + 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2) messages.append({ "type": "voice", "sender": sender, "center": (ax, ay), "y": ay, "is_unread": is_unread, "is_converted": is_converted, "content": converted_trigger_text if is_converted else None }) # B. 添加文本消息 for i, (bbox, text, conf) in enumerate(ocr_results): if i in claimed_ocr_indices: continue c_x = int((bbox[0][0] + bbox[2][0]) / 2) c_y = int((bbox[0][1] + bbox[2][1]) / 2) if c_y < 150 or c_y > h - 100: continue 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)' if len(text) < 20 and (re.search(time_pattern, text) or re.match(r'^[0-9:\s日年月\-]+$', text)): logger.info(f"忽略时间戳/日期文本: '{text}'") continue clean_text = text.strip() if re.match(r'^.?[0-9]{1,2}"?$', clean_text): logger.info(f"忽略疑似时长文本: '{clean_text}'") continue # 噪音判定 (例如 "少3"") if "少" in clean_text and len(clean_text) < 8 and re.search(r'\d', clean_text): logger.info(f"忽略噪音文本: '{clean_text}'") continue if clean_text in MENU_KEYWORDS: logger.info(f"忽略菜单关键词: '{clean_text}'") continue if any(k in clean_text for k in IGNORE_CONTENT): logger.info(f"忽略系统消息内容: '{clean_text}'") continue left_x = bbox[0][0] sender = "对方" if left_x < w * 0.5 else "我" messages.append({ "type": "text", "sender": sender, "content": text.strip(), "center": (c_x, c_y), "y": c_y }) # 6. 排序 messages.sort(key=lambda x: x['y']) return messages, debug_img, chat_title async def analyze_chat_image(image_path, output_path, device=None, target_name="对方", process_strategy="ALL"): """ 全面采用 CV + OCR 识别微信聊天截图中的最后一条消息 :param process_strategy: 语音处理策略 (ALL/UNREAD/LAST) 注意：此函数现在包含一个循环，如果发现需要转文字的语音，会逐个处理并重新截图。 """ try: d = device if device else connect_device() if not d: return [], None current_image_path = image_path current_output_path = output_path final_messages = [] loop_count = 0 MAX_LOOPS = 10 # 增加循环次数上限，适应 ALL 策略 # 统计计数器 total_voices_count = 0 convert_opened_count = 0 convert_closed_count = 0 # 记录本次会话已处理过的语音 Y 坐标集合 processed_y_coords = set() # 记录 Peek-and-Restore 过程中抓取到的语音内容 {y_coord: content} captured_voice_contents = {} # 初始化异步任务列表 analyze_chat_image._ocr_tasks = [] while loop_count < MAX_LOOPS: loop_count += 1 logger.info(f"--- 分析循环 第 {loop_count} 次 ---") # 1. 扫描当前屏幕 messages, debug_img, chat_title = _scan_chat_messages(current_image_path) if messages is None: # 读取失败 return [], None # 更新消息发送者名称 (将 "对方" 替换为 实际标题) if chat_title and chat_title != "对方": for m in messages: if m['sender'] == "对方": m['sender'] = chat_title # 保存当前状态的调试图 if current_output_path: cv2.imwrite(current_output_path, debug_img) logger.info(f"调试图已保存: {current_output_path}") # 2. 筛选需要处理的语音 all_voices = [m for m in messages if m['type'] == 'voice'] all_voices.sort(key=lambda x: x['y']) # 从上到下 # 更新统计 (取当前扫描到的数量) total_voices_count = len(all_voices) # Helper: 检查是否已处理 def is_processed(y_coord): for py in processed_y_coords: if abs(y_coord - py) < 20: # 20px 容差 return True return False target_voices = [] if process_strategy == "ALL": # ALL 策略：处理所有未被记录处理过的、且未转换的语音 target_voices = [m for m in all_voices if not m.get('is_converted') and not is_processed(m['y'])] logger.info(f"策略(ALL): 发现 {len(target_voices)} 条未转换待处理语音") elif process_strategy == "UNREAD": # UNREAD 策略：只处理未读且未转换且未处理过的 target_voices = [m for m in all_voices if m.get('is_unread') and not m.get('is_converted') and not is_processed(m['y'])] logger.info(f"策略(UNREAD): 发现 {len(target_voices)} 条未读待处理语音") elif process_strategy == "LAST": # LAST 策略：只处理最后一条未转换的 unconverted = [m for m in all_voices if not m.get('is_converted')] if unconverted: last_voice = unconverted[-1] if not is_processed(last_voice['y']): target_voices = [last_voice] logger.info(f"策略(LAST): 仅关注最后一条未转换语音") # 如果没有需要处理的语音，或者我们已经达到了策略要求，退出循环 if not target_voices: logger.info("当前屏幕无待处理语音，分析结束") final_messages = messages break # 3. 处理第一条目标语音 # 注意：只处理第一条，因为处理后界面会变动（展开文字），坐标会失效 target = target_voices[0] vx, vy = int(target['center'][0]), int(target['center'][1]) # 标记为已处理 processed_y_coords.add(target['y']) logger.info(f"准备处理语音 ({vx}, {vy})...") # 高亮正在处理的语音并保存更新后的调试图 draw_debug_info(current_output_path, messages, current_voice_center=(vx, vy)) # 执行操作：长按 -> 转文字 logger.info(f"正在长按语音消息 ({vx}, {vy})...") d.long_click(vx, vy, 1.0) # 缩短按压时间 # 轮询寻找“转文字”按钮 logger.info("正在快速寻找'转文字'按钮...") zhuan_template = os.path.join(TEMPLATE_DIR, "zhun_wen_zi.jpg") btn_pos = None poll_start = time.time() while time.time() - poll_start < 3.0: # 最多等 3 秒 menu_shot = get_next_debug_path("step_long_press_poll") d.screenshot(menu_shot) btn_pos = find_template_match(menu_shot, zhuan_template, threshold=0.7) if btn_pos: break time.sleep(0.2) # 快速轮询 if btn_pos: btn_x, btn_y = int(btn_pos[0]), int(btn_pos[1]) logger.info(f"✅ 找到'转文字'按钮: ({btn_x}, {btn_y})，点击中...") safe_device_click(d, btn_x, btn_y) convert_opened_count += 1 logger.info("等待语音转文字完成...") time.sleep(3.0) # 缩短等待时间 (原5.0s) # --- Peek-and-Restore 逻辑 (异步优化版) --- # 1. 截图 (但不立即 OCR，而是丢给异步任务) peek_shot = get_next_debug_path("step_peek_content") d.screenshot(peek_shot) logger.info("已截图，启动异步OCR任务以提取内容...") async def _async_ocr_task(img_path, target_y): """内部异步任务：在线程池中运行 OCR""" loop = asyncio.get_running_loop() # 在默认执行器(线程池)中运行耗时的 _scan_chat_messages msgs, _, _ = await loop.run_in_executor(None, _scan_chat_messages, img_path) found = None for pm in msgs: if pm['type'] == 'voice' and pm.get('is_converted'): if abs(pm['y'] - target_y) < 50: found = pm.get('content') break return target_y, found # 创建并保存任务 task = asyncio.create_task(_async_ocr_task(peek_shot, vy)) # 我们需要一个列表来保存任务，这里临时利用 list if not hasattr(analyze_chat_image, "_ocr_tasks"): analyze_chat_image._ocr_tasks = [] analyze_chat_image._ocr_tasks.append(task) # 2. 立即还原状态 (取消转文字) # 注意：由于 OCR 还没出结果，我们无法精确定位展开后的文字位置 # 但通常点击原语音气泡位置 (vx, vy) 也能触发菜单 logger.info("准备还原状态 (取消转文字)...") d.long_click(vx, vy, 1.0) # 盲点原坐标 logger.info("正在快速寻找'隐藏文字'按钮...") cancel_template = os.path.join(TEMPLATE_DIR, "cancel_zhuan_wen_zi.jpg") cancel_btn = None poll_start = time.time() while time.time() - poll_start < 3.0: restore_menu_shot = get_next_debug_path("step_restore_poll") d.screenshot(restore_menu_shot) cancel_btn = find_template_match(restore_menu_shot, cancel_template, threshold=0.7) if cancel_btn: break time.sleep(0.2) if cancel_btn: cx, cy = int(cancel_btn[0]), int(cancel_btn[1]) logger.info(f"✅ 找到'隐藏文字'按钮: ({cx}, {cy})，点击还原...") safe_device_click(d, cx, cy) convert_closed_count += 1 time.sleep(2.0) # 等待收起动画 else: logger.warning("❌ 未找到'隐藏文字'按钮，无法还原状态！(后续可能导致重复处理)") # 3. 准备下一次循环 # 重新截图，因为界面可能微调，或者只是恢复了 next_screenshot = get_next_debug_path("step_restored") d.screenshot(next_screenshot) current_image_path = next_screenshot current_output_path = get_next_debug_path("flag_restored") continue else: logger.warning("❌ 未找到'转文字'按钮，可能是已转换或误判") # 即使失败，也已记录在 processed_y_coords 中，避免死循环 # 继续尝试下一条语音 logger.info("跳过当前语音，继续扫描...") continue # 循环结束后，等待所有异步 OCR 任务完成 if hasattr(analyze_chat_image, "_ocr_tasks") and analyze_chat_image._ocr_tasks: logger.info(f"等待 {len(analyze_chat_image._ocr_tasks)} 个异步 OCR 任务完成...") results = await asyncio.gather(*analyze_chat_image._ocr_tasks) for y, content in results: if content: captured_voice_contents[y] = content logger.info(f"✅ [Async OCR] 异步获取到语音内容 (y={y}): {content}") # 清空任务列表 analyze_chat_image._ocr_tasks = [] # 循环结束，返回最后一次分析的结果 if not final_messages: # 如果循环因为 max_loops 退出，确保有结果 final_messages = messages # 注入 peek 到的内容 if captured_voice_contents: logger.info(f"正在注入 {len(captured_voice_contents)} 条已还原的语音内容...") for m in final_messages: if m['type'] == 'voice' and not m.get('content'): for py, content in captured_voice_contents.items(): if abs(m['y'] - py) < 30: # 匹配原始 Y 坐标 m['content'] = content m['is_converted'] = True # 标记为逻辑上已转换 logger.info(f" -> 注入内容: {content[:10]}...") break # 构造返回值 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)) 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)) except: input_field_coordinates = (540, 1728) # 找出最后一条消息 last_msg = None if final_messages: final_messages.sort(key=lambda x: x['y']) last_msg = final_messages[-1] # 转换为 dialogue_log 格式 (简单转换，具体业务逻辑在调用方处理) # 注意：T2 需要的是上下文列表 pass # 实际上 T2 使用的是 LLM 上下文构建，这里不需要转换成特定 dict 结构， # 但为了兼容旧接口，我们还是返回 messages 列表给调用者处理， # 或者在这里处理成 (role, content) 列表？ # 原代码似乎没有做太多转换，而是直接返回 messages 列表？ # 仔细看原代码：analyze_chat_image 并没有返回 messages 列表！ # 它返回 dialogue_log, input_pos # 原代码 lines 339-340: dialogue_log = [] # 可以在最后统一生成 # 统一生成 dialogue_log for msg in final_messages: # 尝试注入异步获取的语音内容 if msg['type'] == 'voice': # 模糊匹配 Y 坐标 for y_key, content in captured_voice_contents.items(): if abs(msg['y'] - y_key) < 20: msg['is_converted'] = True msg['content'] = content logger.info(f"注入语音内容到最终消息列表: {content}") break # 只添加有内容的文本消息，或已转换且有内容的语音消息 if msg['type'] == 'text' and msg.get('content'): dialogue_log.append(msg) elif msg['type'] == 'voice' and msg.get('is_converted') and msg.get('content'): dialogue_log.append(msg) logger.info(f"📊 [统计] 语音总数: {total_voices_count}, 打开转文字次数: {convert_opened_count}, 关闭转文字次数: {convert_closed_count}") return dialogue_log, input_field_coordinates except Exception as e: logger.error(f"分析过程发生异常: {e}", exc_info=True) return [], None def clean_screenshots_dir(): """清理截图目录""" if not os.path.exists(OUTPUT_DIR): os.makedirs(OUTPUT_DIR) return for f in os.listdir(OUTPUT_DIR): if f.lower().endswith(('.jpg', '.png', '.jpeg')): try: os.remove(os.path.join(OUTPUT_DIR, f)) except Exception as e: logger.warning(f"Failed to delete {f}: {e}") def is_in_chat_interface(d): """ 检查是否在微信聊天界面 """ try: # 1. 底部语音/键盘切换按钮 if d(description="切换到语音").exists or d(description="切换到键盘").exists: return True # 2. 底部输入框 if d(className="android.widget.EditText").exists: return True # 3. 底部“按住说话”按钮 if d(text="按住说话").exists: return True # 4. 右上角更多按钮 if d(description="聊天信息").exists: return True except Exception as e: logger.warning(f"is_in_chat_interface check failed: {e}") return False def find_input_box_center(image_path): """ 寻找输入框中心坐标 (兜底策略) 优先使用几何特征 (底部 88% 处) """ try: if not os.path.exists(image_path): return (540, 2100), None img = cv2.imread(image_path) if img is None: return (540, 2100), None h, w = img.shape[:2] # 策略：直接返回屏幕底部 88% 处的中心点 center_x = int(w * 0.5) center_y = int(h * 0.88) logger.info(f"find_input_box_center fallback: ({center_x}, {center_y})") return (center_x, center_y), None except Exception as e: logger.error(f"find_input_box_center error: {e}") return (540, 2100), None def find_template_match(screen_path, template_path, threshold=0.8): """ 使用 OpenCV 模板匹配寻找按钮中心坐标 """ try: if not os.path.exists(template_path): logger.error(f"Template file not found: {template_path}") return None img = cv2.imread(screen_path) template = cv2.imread(template_path) if img is None or template is None: return None h, w = template.shape[:2] res = cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res) if max_val >= threshold: center_x = max_loc[0] + w // 2 center_y = max_loc[1] + h // 2 logger.info(f"Template matched! Score: {max_val:.2f}, Center: ({center_x}, {center_y})") return (center_x, center_y) logger.info(f"Template not matched. Max score: {max_val:.2f}") return None except Exception as e: logger.error(f"Template matching failed: {e}") return None def find_all_template_matches(screen_path, template_path, threshold=0.8): """ 使用 OpenCV 模板匹配寻找**所有**符合条件的坐标 """ try: if not os.path.exists(template_path): logger.error(f"Template file not found: {template_path}") return [] img = cv2.imread(screen_path) template = cv2.imread(template_path) if img is None or template is None: return [] h, w = template.shape[:2] res = cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED) # 记录最大匹配度，方便调试阈值 min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res) logger.info(f"模板匹配 {os.path.basename(template_path)}: 最大相似度 = {max_val:.4f} (阈值={threshold})") # 找到所有大于阈值的点 loc = np.where(res >= threshold) points = [] for pt in zip(*loc[::-1]): # Switch collumns and rows center_x = pt[0] + w // 2 center_y = pt[1] + h // 2 points.append((center_x, center_y)) # 简单的去重（非极大值抑制的简化版，合并相近的点） # 这里假设红点不会重叠，暂时直接返回，或者做一个简单的聚类 # 实际应用中，matchTemplate 对同一个目标周围可能会有多个连续的匹配点 # 我们需要合并它们 unique_points = [] for p in points: is_close = False for up in unique_points: if abs(p[0] - up[0]) < 10 and abs(p[1] - up[1]) < 10: is_close = True break if not is_close: unique_points.append(p) if unique_points: logger.info(f"Found {len(unique_points)} matches for {os.path.basename(template_path)}") return unique_points except Exception as e: logger.error(f"find_all_template_matches failed: {e}") return [] def perform_input_action(d, center_point, text, auto_send=True): """ 执行输入操作 """ 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]) 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 # --- 新增逻辑结束 --- # 1. 尝试找到原生输入框并输入 edit_text = d(className="android.widget.EditText") input_success = False if edit_text.exists: logger.info("Found native EditText, using set_text") try: edit_text.click() time.sleep(0.5) edit_text.set_text(text) input_success = True except Exception as e: logger.warning(f"Native input failed: {e}") # 2. 如果原生输入失败，使用坐标点击 + 粘贴/输入 if not input_success: cx, cy = center_point logger.info(f"Using coordinate input: {center_point}") d.click(cx, cy) time.sleep(1.0) try: d.send_keys(text) except Exception: logger.warning("send_keys failed, trying set_clipboard") d.set_clipboard(text) d.click(cx, cy) time.sleep(0.5) # 尝试粘贴 d.press("paste") time.sleep(1.0) # 3. 发送 if auto_send: # 优先使用模板匹配寻找“发送”按钮 logger.info("尝试使用模板匹配寻找'发送'按钮...") tmp_screen = os.path.join(os.path.dirname(os.path.abspath(__file__)), "temp_send_check.jpg") d.screenshot(tmp_screen) # 使用相对路径 template_path = os.path.join(TEMPLATE_DIR, "send.jpg") send_btn_pos = find_template_match(tmp_screen, template_path, threshold=0.7) # 稍微降低阈值以提高召回 if send_btn_pos: logger.info(f"通过模板匹配找到发送按钮: {send_btn_pos}, 点击...") d.click(send_btn_pos[0], send_btn_pos[1]) else: logger.warning("模板匹配未找到发送按钮，尝试原生控件查找...") if d(text="发送").exists: d(text="发送").click() logger.info("Clicked '发送'") else: d.press("enter") logger.info("Pressed Enter") # 清理临时文件 if os.path.exists(tmp_screen): try: os.remove(tmp_screen) except: pass return True except Exception as e: logger.error(f"perform_input_action error: {e}") return False def match_template_center(image_path, template_path, threshold=0.8): """ 使用 OpenCV 模板匹配寻找目标图片中心坐标 """ try: if not os.path.exists(image_path) or not os.path.exists(template_path): logger.error(f"Image or template not found: {image_path}, {template_path}") return None img = cv2.imread(image_path) template = cv2.imread(template_path) if img is None or template is None: logger.error("Failed to read image or template") return None # 转换为灰度图进行匹配 img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) template_gray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY) # 模板匹配 result = cv2.matchTemplate(img_gray, template_gray, cv2.TM_CCOEFF_NORMED) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result) if max_val >= threshold: h, w = template_gray.shape top_left = max_loc center_x = int(top_left[0] + w / 2) center_y = int(top_left[1] + h / 2) logger.info(f"Template matched with confidence {max_val:.2f} at ({center_x}, {center_y})") return (center_x, center_y) else: logger.warning(f"Template match failed. Max confidence: {max_val:.2f} < Threshold: {threshold}") return None except Exception as e: logger.error(f"match_template_center error: {e}") return None 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