# 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) from Util.EasyOcrKit import get_easyocr_reader # 配置日志 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger("TestWeChat") def find_input_box_center(image_path): """ 识别底部输入框的中心坐标，返回 (center_x, center_y, rect_box) rect_box 用于可视化: (x, y, w, h) (相对于原图) """ try: img_data = np.fromfile(image_path, dtype=np.uint8) img = cv2.imdecode(img_data, cv2.IMREAD_COLOR) if img is None: return None, None height, width = img.shape[:2] # 截取底部 12% 区域 (缩小范围以精准定位) bottom_h = int(height * 0.12) crop_y_start = height - bottom_h crop = img[crop_y_start:height, 0:width] # 预处理 gray = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY) # 策略1: Canny 边缘检测 (对低对比度 UI 更有效) edges = cv2.Canny(gray, 50, 150) # 膨胀连接断裂的边缘 kernel = np.ones((3,3), np.uint8) dilated = cv2.dilate(edges, kernel, iterations=1) contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) best_cnt = None max_area = 0 for cnt in contours: x, y, w, h = cv2.boundingRect(cnt) # 筛选条件优化： # 1. 宽度: 屏幕宽度的 50% - 98% (通常输入框很长) # 2. 高度: 30px - 底部区域的 90% if width * 0.5 < w < width * 0.98 and 30 < h < bottom_h * 0.9: # 计算中心点 Y 坐标相对于全图 global_y = crop_y_start + y + h // 2 # 排除过于靠下的区域 (导航栏/手势条)，通常在最后 2% if global_y > height * 0.98: continue if w * h > max_area: max_area = w * h best_cnt = (x, y, w, h) if best_cnt: x, y, w, h = best_cnt center_x = x + w // 2 center_y = crop_y_start + y + h // 2 logger.info(f"找到输入框(CV-Canny): ({center_x}, {center_y}), 尺寸: {w}x{h}") return (center_x, center_y), (x, crop_y_start + y, w, h) # 策略2: 自适应阈值 (原有逻辑作为备份) thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2) contours_thresh, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for cnt in contours_thresh: x, y, w, h = cv2.boundingRect(cnt) if width * 0.4 < w < width * 0.95 and 20 < h < bottom_h * 0.8: global_y = crop_y_start + y + h // 2 if global_y > height * 0.98: continue if w * h > max_area: max_area = w * h best_cnt = (x, y, w, h) if best_cnt: x, y, w, h = best_cnt center_x = x + w // 2 center_y = crop_y_start + y + h // 2 logger.info(f"找到输入框(CV-Adaptive): ({center_x}, {center_y}), 尺寸: {w}x{h}") return (center_x, center_y), (x, crop_y_start + y, w, h) # 兜底策略：使用更靠下的默认坐标 (94%) # 之前的 90.5% 用户反馈偏上 logger.warning("未找到明显输入框轮廓，使用更靠下的默认坐标 (94%)") default_y = int(height * 0.94) center_x = width // 2 # 构造假想框 fake_w = int(width * 0.7) fake_h = int(height * 0.08) # 稍微加高一点，视觉上更像 fake_x = (width - fake_w) // 2 fake_y = default_y - fake_h // 2 return (center_x, default_y), (fake_x, fake_y, fake_w, fake_h) except Exception as e: logger.error(f"查找输入框失败: {e}") return None, None def find_send_button(d): """ 截图并寻找发送按钮 (绿色按钮) 扩大搜索范围以适应键盘弹出的情况 """ try: # 截图到 Screenshots 目录方便调试 screenshot_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Screenshots") if not os.path.exists(screenshot_dir): os.makedirs(screenshot_dir) timestamp = time.strftime("%Y%m%d_%H%M%S") debug_shot_path = os.path.join(screenshot_dir, f"debug_send_check_{timestamp}.jpg") d.screenshot(debug_shot_path) logger.info(f"发送按钮查找调试截图已保存: {debug_shot_path}") img = cv2.imread(debug_shot_path) if img is None: return None h, w = img.shape[:2] # ROI: 底部 60% (考虑到键盘弹出，按钮可能被顶上去) # 且只关注右侧 30% roi_h = int(h * 0.6) roi_w = int(w * 0.3) y_start = h - roi_h x_start = w - roi_w roi = img[y_start:h, x_start:w] # 转换 HSV hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) # 绿色范围 (WeChat Green) lower_green = np.array([35, 80, 80]) upper_green = np.array([90, 255, 255]) mask = cv2.inRange(hsv, lower_green, upper_green) # 查找轮廓 contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) if contours: # 找符合条件的轮廓 valid_candidates = [] for cnt in contours: area = cv2.contourArea(cnt) x, y, cw, ch = cv2.boundingRect(cnt) # 过滤太小的噪点和太大的区域(例如全屏背景) # 发送按钮通常面积在 2000-15000 之间 (视分辨率而定) if 500 < area < 30000: # 宽高比检查：发送按钮通常接近正方形或微扁 (ratio < 2.5) ratio = float(cw) / ch if 0.5 < ratio < 3.0: # 坐标还原到原图 global_y = y_start + y valid_candidates.append({ 'cnt': cnt, 'area': area, 'y': global_y, 'rect': (x, y, cw, ch) }) if valid_candidates: # 核心逻辑：发送按钮一定是所有绿色元素中最靠下的 (Y坐标最大) # 且在最右侧 # 先按 Y 坐标降序排序 valid_candidates.sort(key=lambda c: c['y'], reverse=True) # 取最靠下的一个 (可能是发送按钮) best = valid_candidates[0] # 获取中心点 bx, by, bw, bh = best['rect'] cx = x_start + bx + bw // 2 cy = y_start + by + bh // 2 logger.info(f"通过图像识别找到发送按钮 (Bottom-Most): ({cx}, {cy}), 面积: {best['area']}") return cx, cy logger.warning("未通过图像识别找到绿色发送按钮") return None except Exception as e: logger.error(f"查找发送按钮出错: {e}") return None def take_debug_screenshot(d, step_name): """ 调试专用截图函数 """ try: screenshot_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Screenshots") if not os.path.exists(screenshot_dir): os.makedirs(screenshot_dir) timestamp = time.strftime("%Y%m%d_%H%M%S") filename = f"debug_{timestamp}_{step_name}.jpg" save_path = os.path.join(screenshot_dir, filename) d.screenshot(save_path) logger.info(f"📸 [调试截图] {step_name} 已保存: {filename}") return save_path except Exception as e: logger.error(f"截图失败 ({step_name}): {e}") return None def perform_input_action(coords, text): """ 点击坐标并输入文本 """ # 优先尝试使用 uiautomator2 的原生控件查找 (更稳健) native_success = False try: d = u2.connect() # 查找 EditText 控件 input_elem = d(className="android.widget.EditText") if input_elem.exists: logger.info("发现原生输入框控件，尝试点击...") # 1. 截图：点击前 take_debug_screenshot(d, "native_01_before_click") # 双击策略 input_elem.click() time.sleep(0.5) input_elem.click() time.sleep(1) # 2. 截图：点击后 (预期键盘弹出) take_debug_screenshot(d, "native_02_after_click_keyboard") logger.info(f"输入文本: {text}") # 尝试 set_text + send_keys 组合 try: input_elem.set_text(text) except: pass time.sleep(0.5) # 检查文本是否输入成功，如果没有，尝试 send_keys try: current_text = input_elem.get_text() if not current_text or current_text != text: logger.warning(f"set_text 似乎未生效 (当前: {current_text})，尝试 send_keys...") d.send_keys(text) except: d.send_keys(text) # 3. 截图：输入文本后 take_debug_screenshot(d, "native_03_after_text_input") # 尝试发送回车键 time.sleep(0.5) d.press("enter") # 尝试点击发送按钮 try: if d(text="发送").exists: d(text="发送").click() logger.info("已点击 '发送' 按钮 (Native Text)") take_debug_screenshot(d, "native_04_after_send_click_text") else: logger.info("未找到 '发送' 文本控件，尝试图像识别...") send_btn_coords = find_send_button(d) if send_btn_coords: sx, sy = send_btn_coords d.click(sx, sy) logger.info(f"已点击 '发送' 按钮 (Image Rec): {sx}, {sy}") take_debug_screenshot(d, "native_04_after_send_click_image") else: width, height = d.window_size() fallback_x = int(width * 0.9) fallback_y = int(height * 0.965) logger.info(f"未识别到发送按钮，尝试盲点右下角: {fallback_x}, {fallback_y}") d.click(fallback_x, fallback_y) take_debug_screenshot(d, "native_04_after_send_click_fallback") except Exception as e: logger.error(f"点击发送按钮失败: {e}") logger.info("输入完成 (Native)") native_success = True return else: logger.warning("未找到输入框元素 (Native)，转入坐标点击模式...") except Exception as e: logger.warning(f"原生控件操作失败，降级为坐标点击: {e}") if native_success: return # 降级方案：使用坐标点击 if not coords: logger.error("坐标无效，无法执行点击输入") return x, y = coords # 坐标安全检查 if 'd' not in locals(): d = u2.connect() try: width, height = d.window_size() # 移除过于激进的坐标修正 (95% -> 93% 会导致点到输入框上方) # 输入框中心通常在 96%-97% 左右 if y > height * 0.99: logger.warning(f"检测到的输入框坐标 y={y} 过于靠底，修正为 {height * 0.97}") y = int(height * 0.97) except: pass try: logger.info(f"设备连接成功: {d.info.get('serial')}") # 1. 截图：点击输入框前 take_debug_screenshot(d, "coord_01_before_click_input") # 点击输入框 logger.info(f"点击坐标: ({x}, {y})") d.click(x, y) time.sleep(0.5) d.click(x, y) # Double click # 等待键盘弹出 time.sleep(1.5) # 2. 截图：点击输入框后 take_debug_screenshot(d, "coord_02_after_click_input_keyboard") # 输入文本 logger.info(f"输入文本 (SendKeys): {text}") try: d.send_keys(text) except Exception as sk_e: logger.error(f"send_keys 失败: {sk_e}") # 3. 截图：输入文本后 take_debug_screenshot(d, "coord_03_after_input_text") time.sleep(0.5) d.press("enter") # 尝试查找发送按钮并点击 try: if d(text="发送").exists: d(text="发送").click() logger.info("已点击 '发送' 按钮 (Native Text)") take_debug_screenshot(d, "coord_04_after_click_send_native") else: logger.info("未找到 '发送' 文本控件，尝试图像识别...") send_btn_coords = find_send_button(d) if send_btn_coords: sx, sy = send_btn_coords d.click(sx, sy) logger.info(f"已点击 '发送' 按钮 (Image Rec): {sx}, {sy}") take_debug_screenshot(d, "coord_04_after_click_send_image") else: width, height = d.window_size() fallback_x = int(width * 0.9) fallback_y = int(height * 0.965) logger.info(f"未识别到发送按钮，尝试盲点右下角: {fallback_x}, {fallback_y}") d.click(fallback_x, fallback_y) take_debug_screenshot(d, "coord_04_after_click_send_fallback") except Exception as e: logger.error(f"点击发送按钮失败: {e}") logger.info("输入完成 (Coordinate)") except Exception as e: logger.error(f"自动化操作失败: {e}") def analyze_chat_image(image_path, output_path, target_name="对方"): """ 识别微信聊天截图中的头像并画框，识别对话内容 """ logger.info(f"正在分析图片: {image_path}") # 读取图片（支持中文路径） try: img_data = np.fromfile(image_path, dtype=np.uint8) img = cv2.imdecode(img_data, cv2.IMREAD_COLOR) except Exception as e: logger.error(f"读取图片失败: {e}") return if img is None: logger.error("图片读取为空") return # 备份一份干净的图片用于 OCR (避免识别到画上去的框) img_clean = img.copy() height, width = img.shape[:2] logger.info(f"图片尺寸: {width}x{height}") # 调用输入框识别，获取坐标和可视化框 input_center, input_rect = find_input_box_center(image_path) # 1. 预处理 # 转为灰度图 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 使用自适应阈值二值化 thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2) # 形态学操作：闭运算，填充内部空洞 kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel) # 2. 轮廓查找 contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) logger.info(f"检测到轮廓数量: {len(contours)}") # 收集所有符合条件的头像 avatars = [] # 3. 筛选轮廓 for contour in contours: # 获取外接矩形 x, y, w, h = cv2.boundingRect(contour) # 筛选条件优化： # 1. 形状接近正方形 (放宽宽高比限制: 0.8 ~ 1.2) aspect_ratio = float(w) / h # 2. 尺寸适中 # 假设头像宽度在屏幕宽度的 6% 到 15% 之间 min_w = width * 0.06 max_w = width * 0.15 # 3. 位置筛选 # 排除底部输入框区域 (假设底部 10% 为输入区域) if y > height * 0.9: continue # 左侧头像：靠左边 (x < width * 0.18) # 右侧头像：靠右边 (x > width * 0.82) is_left = x < width * 0.18 is_right = x > width * 0.82 if 0.8 <= aspect_ratio <= 1.2 and min_w < w < max_w: if is_left or is_right: side = "Left" if is_left else "Right" avatars.append({ 'x': x, 'y': y, 'w': w, 'h': h, 'side': side }) # 按 y 坐标排序 avatars.sort(key=lambda a: a['y']) logger.info(f"找到有效头像数量: {len(avatars)}") # 初始化 OCR try: reader = get_easyocr_reader(gpu=True) logger.info("OCR 初始化成功") except Exception as e: logger.error(f"OCR 初始化失败: {e}") reader = None dialogue_log = [] # 4. 绘制对话内容框 (Green/Red Boxes) if avatars: i = 0 while i < len(avatars): current_group_start = i current_side = avatars[i]['side'] # 找到当前组的结束位置 (即下一个不同侧头像的索引) j = i + 1 while j < len(avatars) and avatars[j]['side'] == current_side: j += 1 # Start Y: 当前组第一个头像的上方 (例如 -10px) start_y = max(0, avatars[i]['y'] - 10) # End Y if j < len(avatars): end_y = max(start_y + 10, avatars[j]['y'] - 30) else: # 最后一个框的底边，使用输入框的上沿 if input_rect: _, input_y, _, _ = input_rect end_y = max(start_y + 10, input_y - 10) else: end_y = int(height * 0.9) # 默认 # 绘制大框 # 左侧 (Left) -> 对方 -> 绿色 (0, 255, 0) # 右侧 (Right) -> 我 -> 红色 (0, 0, 255) box_color = (0, 255, 0) if current_side == "Left" else (0, 0, 255) cv2.rectangle(img, (0, start_y), (width, end_y), box_color, 5) logger.info(f"绘制内容框: 侧别={current_side}, 范围 Y={start_y} to {end_y}") # OCR 识别区域文本 if reader: try: safe_start_y = max(0, start_y) safe_end_y = min(height, end_y) if safe_end_y > safe_start_y: roi_img = img_clean[safe_start_y:safe_end_y, 0:width] # 识别 results = reader.read_text(roi_img) # 过滤关键词 ignore_keywords = ["点击查看对话内容", "以上是打招呼的消息", "和 Kimi 的对话", "Kim智能助手"] texts = [] for res in results: text = res[1] prob = res[2] if prob > 0.3: # 1. 检查是否包含屏蔽词 if any(kw in text for kw in ignore_keywords): continue # 2. 检查是否为单行时间 if re.match(r'^\s*\d{1,2}:\d{2}\s*$', text): continue texts.append(text) combined_text = " ".join(texts) if combined_text.strip(): role = target_name if current_side == "Left" else "我" dialogue_log.append(f"{role}: {combined_text}") logger.info(f" -> OCR结果: {combined_text}") except Exception as e: logger.error(f" -> OCR出错: {e}") # 移动到下一组 i = j # 5. 绘制头像框 (Blue/Yellow Boxes) - 画在内容框之上 for av in avatars: x, y, w, h = av['x'], av['y'], av['w'], av['h'] color = (255, 0, 0) if av['side'] == "Left" else (0, 255, 255) cv2.rectangle(img, (x, y), (x + w, y + h), color, 10) logger.info(f"绘制头像: 位置=({x},{y}), 侧别={av['side']}") # 6. 保存结果 try: # 如果有识别到输入框，画出来 (紫色) if input_rect: rx, ry, rw, rh = input_rect cv2.rectangle(img, (rx, ry), (rx + rw, ry + rh), (255, 0, 255), 5) # 画中心点 if input_center: cx, cy = input_center cv2.circle(img, (cx, cy), 10, (255, 0, 255), -1) logger.info(f"已绘制输入框标记: {input_rect}") # cv2.imwrite 不支持中文路径，使用 imencode + tofile ext = os.path.splitext(output_path)[1] cv2.imencode(ext, img)[1].tofile(output_path) logger.info(f"✅ 分析结果已保存至: {output_path}") except Exception as e: logger.error(f"保存分析图片失败: {e}") logger.info("\n" + "="*30) logger.info("对话内容汇总:") for line in dialogue_log: logger.info(line) logger.info("="*30 + "\n") def main(): logger.info("开始执行微信搜索测试...") # 连接设备 try: d = u2.connect() logger.info(f"设备连接成功: {d.info.get('serial')}") except Exception as e: logger.error(f"设备连接失败: {e}") return # 1. 启动微信 logger.info("步骤 1: 启动微信...") d.app_start("com.tencent.mm", stop=True) # 等待微信启动完成 time.sleep(5) # 获取屏幕尺寸 w, h = d.window_size() logger.info(f"屏幕尺寸: {w}x{h}") # 2. 点击搜索按钮 (参考 Opener.py 的坐标比例: w * 0.84, h * 0.08) search_x = int(w * 0.84) search_y = int(h * 0.08) logger.info(f"步骤 2: 点击搜索按钮 (坐标: {search_x}, {search_y})...") d.click(search_x, search_y) time.sleep(2) # 3. 输入 "糖豆爸爸" target_name = "糖豆爸爸" logger.info(f"步骤 3: 输入搜索内容 '{target_name}'...") try: # 启用 FastInputIME 以支持中文输入 d.set_input_ime(True) # 点击搜索框获取焦点 (参考 Opener.py: w * 0.4, h * 0.08) d.click(int(w * 0.4), int(h * 0.08)) time.sleep(1) # 输入文字 d.send_keys(target_name) time.sleep(2) # 恢复输入法 d.set_input_ime(False) except Exception as e: logger.error(f"输入文字失败: {e}") try: d(focused=True).set_text(target_name) except: pass # 4. 点击搜索结果 logger.info("步骤 4: 查找并点击搜索结果...") time.sleep(2) found = False # 策略 A: 精确匹配文本 if d(text=target_name).exists: logger.info(f"找到文本为 '{target_name}' 的元素，点击...") d(text=target_name).click() found = True else: logger.warning(f"未找到文本为 '{target_name}' 的元素，尝试模糊匹配或坐标点击...") # 策略 B: 坐标点击 (参考 Opener.py 点击第一个结果: w * 0.5, h * 0.18) result_x = int(w * 0.5) result_y = int(h * 0.18) logger.info(f"尝试点击第一个搜索结果位置 ({result_x}, {result_y})...") d.click(result_x, result_y) found = True if found: logger.info("✅ 已执行点击操作，应该已进入对话框。") # 5. 截图保存结果 logger.info("步骤 5: 截图并分析...") # 等待界面加载稳定 time.sleep(3) # 创建截图目录 screenshot_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Screenshots") if not os.path.exists(screenshot_dir): os.makedirs(screenshot_dir) # 生成文件名 timestamp = time.strftime("%Y%m%d_%H%M%S") filename = f"chat_result_{timestamp}.jpg" save_path = os.path.join(screenshot_dir, filename) try: d.screenshot(save_path) logger.info(f"✅ 原始截图已保存: {save_path}") # 6. 分析截图并标记头像 logger.info("步骤 6: 自动标记头像和识别内容...") analyzed_filename = f"chat_result_{timestamp}_analyzed.jpg" analyzed_path = os.path.join(screenshot_dir, analyzed_filename) analyze_chat_image(save_path, analyzed_path, target_name=target_name) # 7. 查找输入框并执行自动化操作 logger.info("步骤 7: 自动回复...") # 注意：analyze_chat_image 内部已经调用了 find_input_box_center 并且画在图上了 # 但我们需要返回值来执行操作。analyze_chat_image 并没有返回坐标。 # 为了简单，再次调用一次 (或者修改 analyze_chat_image 返回坐标，但改动较大) # 这里直接再次调用 find_input_box_center 获取坐标 coords, _ = find_input_box_center(save_path) if coords: logger.info(f"输入框坐标: {coords}") perform_input_action(coords, "AI助手我现在可以开始和你聊天了！") else: logger.warning("未找到输入框坐标，跳过回复") except Exception as e: logger.error(f"❌ 截图或分析失败: {e}") else: logger.error("❌ 未能定位到搜索结果，跳过截图。") logger.info("测试结束。") if __name__ == "__main__": main()