aiData/Test/T1_StartWeiXin.py

# 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()