# coding=utf-8 import uiautomator2 as u2 import time 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 async def analyze_chat_image(image_path, output_path, device=None, target_name="对方", only_process_last_voice=False): """ 全面采用 CV + OCR 识别微信聊天截图中的最后一条消息 不再使用 VLM :param only_process_last_voice: 如果为 True，仅处理（转文字）屏幕上最后一条未转换的语音消息 """ try: # 1. 初始化 d = device if device else connect_device() if not d: return [], None # 2. 读取图片 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) # 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"] # 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 (从 180 改为 100) 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) # 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) # 青色实心圆表示原始匹配点 # 过滤掉顶部和底部的非聊天区域 # 顶部标题栏通常在 150 像素以内 # 底部输入栏通常在 100 像素以内 (捕捉最底部的文字) 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) # 灰色框表示被过滤 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 # 根据已读/未读画框：未读红框，已读绿框 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) # --- 改进：判断是否已转文字 --- is_converted = False converted_trigger_text = "" for bbox, text, conf in ocr_results: c_x = int((bbox[0][0] + bbox[2][0]) / 2) c_y = int((bbox[0][1] + bbox[2][1]) / 2) # 判定逻辑： # 1. 转换后的文字通常在语音图标下方 30-600 像素 (增加到 600 以适配长文本) # 2. 水平位置偏移在 600 像素内 (增加到 600 以适配宽文本泡) # 3. 关键：确保这两个坐标之间没有其他的语音图标（防止第一个语音“偷”了第二个语音的文字） if 30 < c_y - ay < 600 and abs(c_x - ax) < 600: # 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: # 增加 20 像素缓冲区，防止判定到自身或极近的干扰点 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 (如 13:49, 09:26) # 增强：同时支持 "昨天 14:15" 这种格式 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 (如 5", 3", 少3") # 增强：允许前面有少量杂讯字符，只要结尾是数字或 " is_duration = re.search(r'\d{1,2}"?$', clean_text) and len(clean_text) < 6 # 排除掉语音时长、时间戳和菜单关键字的干扰 if not is_duration and not is_timestamp and clean_text not in MENU_KEYWORDS: is_converted = True converted_trigger_text = clean_text logger.info(f"语音({ax},{ay}) 判定为已转换，关联到有效文本: '{clean_text}'") break else: if is_timestamp: logger.info(f"语音({ax},{ay}) 忽略下方时间戳文本: '{clean_text}'") elif is_duration: logger.info(f"语音({ax},{ay}) 忽略时长文本: '{clean_text}'") if is_converted: logger.info(f"语音消息 ({ax}, {ay}) 已有转换文字: '{converted_trigger_text}'，跳过") # --- 恢复绘图反馈 --- # 根据已读/未读画框：未读红框，已读绿框 color = (0, 0, 255) if is_unread else (0, 255, 0) cv2.rectangle(debug_img, (ax-30, ay-30), (ax+30, ay+30), color, 2) label = "YES" if is_converted else "NO" # 在框的右侧标注 YES 或 NO cv2.putText(debug_img, label, (ax + 40, ay + 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2) # --- 结束 --- messages.append({ "type": "voice", "sender": sender, "center": (ax, ay), "y": ay, "is_unread": is_unread, "is_converted": is_converted, "content": None }) # B. 添加文本消息 for bbox, text, conf in ocr_results: c_x = int((bbox[0][0] + bbox[2][0]) / 2) c_y = int((bbox[0][1] + bbox[2][1]) / 2) # 过滤区域 (顶部标题栏和底部输入栏) # 底部输入栏通常在 100 像素以内 (捕捉最底部的文字) if c_y < 150 or c_y > h - 100: continue # 过滤掉明显的系统词 (通常是日期或时间) time_pattern = r'(\d{4}年|\d{1,2}月|\d{1,2}日|\d{1,2}:\d{2}|昨天|今天|星期|上午|下午|晚上)' if len(text) < 20 and (re.search(time_pattern, text) or re.match(r'^[0-9:\s日年月\-]+$', text)): continue # 排除干扰：语音时长、菜单关键字、系统提示 clean_text = text.strip() if re.match(r'^.?[0-9]{1,2}"?$', clean_text): continue if clean_text in MENU_KEYWORDS: continue if "撤回了一条消息" in text or "打招呼的消息" in text: continue # 改进发送者判定：查看文本块的左边界 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. 排序并找出最后一条消息 if not messages: logger.warning("未发现任何消息") if output_path: cv2.imwrite(output_path, debug_img) return [], None # 按 Y 坐标从上到下排序 messages.sort(key=lambda x: x['y']) last_msg = messages[-1] if output_path: cv2.imwrite(output_path, debug_img) logger.info(f"调试图已保存: {output_path}") dialogue_log = [] input_field_coordinates = (w // 2, int(h * 0.9)) # 默认输入框位置 # 7. 自动处理所有尚未转换的语音消息 # 获取所有语音消息（不论已读未读，只要没转换成文字就处理） unconverted_voices = [m for m in messages if m['type'] == 'voice' and not m.get('is_converted')] # 按 Y 坐标排序，确保从上到下顺序 unconverted_voices.sort(key=lambda x: x['y']) if unconverted_voices: if only_process_last_voice: logger.info(f"策略限制：仅处理最后一条未转换语音 (共发现 {len(unconverted_voices)} 条)") unconverted_voices = [unconverted_voices[-1]] else: logger.info(f"发现 {len(unconverted_voices)} 条未转换的语音，开始全部处理...") for v_msg in unconverted_voices: vx, vy = int(v_msg['center'][0]), int(v_msg['center'][1]) logger.info(f"--- 正在处理语音消息 ({vx}, {vy}) ---") # A. 长按语音 logger.info(f"正在长按语音消息 ({vx}, {vy})...") d.long_click(vx, vy, 1.5) time.sleep(1.5) # B. 截图寻找“转文字”按钮 menu_shot = get_next_debug_path("step_long_press") d.screenshot(menu_shot) zhuan_template = os.path.join(TEMPLATE_DIR, "zhun_wen_zi.jpg") btn_pos = find_template_match(menu_shot, zhuan_template, threshold=0.7) 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) # 等待转换完成 logger.info("等待语音转文字完成...") time.sleep(5.0) # C. 再次截图 OCR 获取转换后的文字 after_convert_shot = get_next_debug_path("step_convert_result") try: d.screenshot(after_convert_shot) convert_ocr = ocr_kit.read_text(after_convert_shot) except Exception as e: logger.error(f"截图或 OCR 失败: {e}") convert_ocr = [] # ... (中间提取文字逻辑不变) ... # D. (已移除) 不再执行“取消转文字”操作，保留文字以避免重复识别 # 之前此处会执行 long_click -> cancel_template -> safe_device_click # 为了解决“反复打开”的问题，现在改为保留转出来的文字 logger.info("保留语音转换后的文字，不进行恢复界面操作") else: logger.warning("❌ 未能找到'转文字'按钮，点击空白处退出") safe_device_click(d, vx + 300, vy) # 8. 重新排序并生成完整的对话日志 # 先合并已经处理好的语音消息内容 # 排除掉转换文字本身产生的 OCR 文本干扰（如果 OCR 识别结果包含在文本消息中，需要过滤） final_messages = [] # 1. 识别并归档所有属于语音转换出来的文字 for v_msg in messages: if v_msg['type'] == 'voice': vx, vy = v_msg['center'] v_content_blocks = [] # 找出所有在语音图标下方且水平相近的文本块，且发送者一致 for msg in messages: if msg['type'] == 'text': cx, cy = msg['center'] # 1. 垂直距离在合理范围内 (30 到 600 像素) # 2. 水平偏移在合理范围内 (增加到 600 像素以适配宽文本泡) # 3. 关键：确保这两个坐标之间没有其他的语音图标（防止第一个语音“偷”了第二个语音的文字） v_dist = cy - vy h_dist = abs(cx - vx) if 30 < v_dist < 600 and h_dist < 600: # 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 vy < other_ay < cy: has_intermediate_audio = True break if has_intermediate_audio: continue # 发送者判定 if msg['sender'] == v_msg['sender']: v_content_blocks.append(msg) msg['is_voice_part'] = True logger.info(f"关联成功: 语音({vx}, {vy}) -> 文本('{msg['content']}') [h_dist={h_dist:.1f}, v_dist={v_dist:.1f}]") # 如果有内容块，按 Y 排序并合并 if v_content_blocks: v_content_blocks.sort(key=lambda x: x['y']) combined_content = "".join([m['content'] for m in v_content_blocks]) v_msg['content'] = combined_content v_msg['is_converted'] = True else: if not v_msg.get('content'): logger.warning(f"语音({vx}, {vy}) 未能关联到任何文本块") # 2. 收集最终要显示的消息（排除被标记为语音部分的文本） for msg in messages: if msg['type'] == 'text': if not msg.get('is_voice_part', False): final_messages.append(msg) else: final_messages.append(msg) # 按 Y 坐标排序 final_messages.sort(key=lambda x: x['y']) # 格式化输出到控制台 print("\n" + "="*50) print(" --- 微信聊天记录提取结果 ---") print("="*50) dialogue_log = [] for msg in final_messages: sender = msg['sender'] content = msg.get('content') or (msg.get('text') if 'text' in msg else "[未识别内容]") if msg['type'] == 'voice': content = f"[语音] {content}" log_line = f"{sender}: {content}" dialogue_log.append(log_line) print(log_line) print("="*50 + "\n") return dialogue_log, input_field_coordinates except Exception as e: logger.error(f"analyze_chat_image 失败: {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