726 lines
27 KiB
Python
726 lines
27 KiB
Python
# coding=utf-8
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import uiautomator2 as u2
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import time
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import logging
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import sys
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import os
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import cv2
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import numpy as np
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import re
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# 添加项目根目录到 sys.path 以便导入 Util
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project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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if project_root not in sys.path:
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sys.path.append(project_root)
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from Util.EasyOcrKit import get_easyocr_reader
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# 配置日志
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger("TestWeChat")
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def find_input_box_center(image_path):
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"""
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识别底部输入框的中心坐标，返回 (center_x, center_y, rect_box)
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rect_box 用于可视化: (x, y, w, h) (相对于原图)
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"""
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try:
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img_data = np.fromfile(image_path, dtype=np.uint8)
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img = cv2.imdecode(img_data, cv2.IMREAD_COLOR)
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if img is None:
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return None, None
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height, width = img.shape[:2]
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# 截取底部 12% 区域 (缩小范围以精准定位)
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bottom_h = int(height * 0.12)
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crop_y_start = height - bottom_h
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crop = img[crop_y_start:height, 0:width]
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# 预处理
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gray = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY)
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# 策略1: Canny 边缘检测 (对低对比度 UI 更有效)
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edges = cv2.Canny(gray, 50, 150)
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# 膨胀连接断裂的边缘
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kernel = np.ones((3,3), np.uint8)
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dilated = cv2.dilate(edges, kernel, iterations=1)
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contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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best_cnt = None
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max_area = 0
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for cnt in contours:
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x, y, w, h = cv2.boundingRect(cnt)
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# 筛选条件优化：
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# 1. 宽度: 屏幕宽度的 50% - 98% (通常输入框很长)
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# 2. 高度: 30px - 底部区域的 90%
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if width * 0.5 < w < width * 0.98 and 30 < h < bottom_h * 0.9:
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# 计算中心点 Y 坐标相对于全图
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global_y = crop_y_start + y + h // 2
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# 排除过于靠下的区域 (导航栏/手势条)，通常在最后 2%
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if global_y > height * 0.98:
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continue
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if w * h > max_area:
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max_area = w * h
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best_cnt = (x, y, w, h)
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if best_cnt:
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x, y, w, h = best_cnt
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center_x = x + w // 2
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center_y = crop_y_start + y + h // 2
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logger.info(f"找到输入框(CV-Canny): ({center_x}, {center_y}), 尺寸: {w}x{h}")
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return (center_x, center_y), (x, crop_y_start + y, w, h)
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# 策略2: 自适应阈值 (原有逻辑作为备份)
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thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
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cv2.THRESH_BINARY_INV, 11, 2)
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contours_thresh, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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for cnt in contours_thresh:
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x, y, w, h = cv2.boundingRect(cnt)
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if width * 0.4 < w < width * 0.95 and 20 < h < bottom_h * 0.8:
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global_y = crop_y_start + y + h // 2
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if global_y > height * 0.98:
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continue
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if w * h > max_area:
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max_area = w * h
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best_cnt = (x, y, w, h)
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if best_cnt:
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x, y, w, h = best_cnt
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center_x = x + w // 2
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center_y = crop_y_start + y + h // 2
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logger.info(f"找到输入框(CV-Adaptive): ({center_x}, {center_y}), 尺寸: {w}x{h}")
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return (center_x, center_y), (x, crop_y_start + y, w, h)
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# 兜底策略：使用更靠下的默认坐标 (94%)
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# 之前的 90.5% 用户反馈偏上
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logger.warning("未找到明显输入框轮廓，使用更靠下的默认坐标 (94%)")
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default_y = int(height * 0.94)
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center_x = width // 2
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# 构造假想框
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fake_w = int(width * 0.7)
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fake_h = int(height * 0.08) # 稍微加高一点，视觉上更像
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fake_x = (width - fake_w) // 2
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fake_y = default_y - fake_h // 2
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return (center_x, default_y), (fake_x, fake_y, fake_w, fake_h)
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except Exception as e:
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logger.error(f"查找输入框失败: {e}")
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return None, None
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def find_send_button(d):
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"""
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截图并寻找发送按钮 (绿色按钮)
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扩大搜索范围以适应键盘弹出的情况
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"""
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try:
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# 截图到 Screenshots 目录方便调试
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screenshot_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Screenshots")
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if not os.path.exists(screenshot_dir):
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os.makedirs(screenshot_dir)
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timestamp = time.strftime("%Y%m%d_%H%M%S")
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debug_shot_path = os.path.join(screenshot_dir, f"debug_send_check_{timestamp}.jpg")
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d.screenshot(debug_shot_path)
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logger.info(f"发送按钮查找调试截图已保存: {debug_shot_path}")
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img = cv2.imread(debug_shot_path)
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if img is None:
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return None
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h, w = img.shape[:2]
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# ROI: 底部 60% (考虑到键盘弹出，按钮可能被顶上去)
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# 且只关注右侧 30%
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roi_h = int(h * 0.6)
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roi_w = int(w * 0.3)
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y_start = h - roi_h
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x_start = w - roi_w
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roi = img[y_start:h, x_start:w]
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# 转换 HSV
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hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
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# 绿色范围 (WeChat Green)
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lower_green = np.array([35, 80, 80])
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upper_green = np.array([90, 255, 255])
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mask = cv2.inRange(hsv, lower_green, upper_green)
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# 查找轮廓
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if contours:
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# 找符合条件的轮廓
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valid_candidates = []
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for cnt in contours:
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area = cv2.contourArea(cnt)
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x, y, cw, ch = cv2.boundingRect(cnt)
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# 过滤太小的噪点和太大的区域(例如全屏背景)
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# 发送按钮通常面积在 2000-15000 之间 (视分辨率而定)
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if 500 < area < 30000:
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# 宽高比检查：发送按钮通常接近正方形或微扁 (ratio < 2.5)
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ratio = float(cw) / ch
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if 0.5 < ratio < 3.0:
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# 坐标还原到原图
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global_y = y_start + y
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valid_candidates.append({
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'cnt': cnt,
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'area': area,
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'y': global_y,
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'rect': (x, y, cw, ch)
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})
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if valid_candidates:
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# 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)
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# 且在最右侧
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# 先按 Y 坐标降序排序
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valid_candidates.sort(key=lambda c: c['y'], reverse=True)
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# 取最靠下的一个 (可能是发送按钮)
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best = valid_candidates[0]
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# 获取中心点
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bx, by, bw, bh = best['rect']
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cx = x_start + bx + bw // 2
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cy = y_start + by + bh // 2
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logger.info(f"通过图像识别找到发送按钮 (Bottom-Most): ({cx}, {cy}), 面积: {best['area']}")
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return cx, cy
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logger.warning("未通过图像识别找到绿色发送按钮")
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return None
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except Exception as e:
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logger.error(f"查找发送按钮出错: {e}")
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return None
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def take_debug_screenshot(d, step_name):
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"""
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调试专用截图函数
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"""
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try:
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screenshot_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Screenshots")
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if not os.path.exists(screenshot_dir):
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os.makedirs(screenshot_dir)
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timestamp = time.strftime("%Y%m%d_%H%M%S")
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filename = f"debug_{timestamp}_{step_name}.jpg"
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save_path = os.path.join(screenshot_dir, filename)
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d.screenshot(save_path)
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logger.info(f"📸 [调试截图] {step_name} 已保存: {filename}")
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return save_path
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except Exception as e:
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logger.error(f"截图失败 ({step_name}): {e}")
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return None
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def perform_input_action(coords, text):
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"""
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点击坐标并输入文本
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"""
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# 优先尝试使用 uiautomator2 的原生控件查找 (更稳健)
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native_success = False
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try:
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d = u2.connect()
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# 查找 EditText 控件
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input_elem = d(className="android.widget.EditText")
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if input_elem.exists:
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logger.info("发现原生输入框控件，尝试点击...")
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# 1. 截图：点击前
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take_debug_screenshot(d, "native_01_before_click")
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# 双击策略
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input_elem.click()
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time.sleep(0.5)
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input_elem.click()
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time.sleep(1)
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# 2. 截图：点击后 (预期键盘弹出)
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take_debug_screenshot(d, "native_02_after_click_keyboard")
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logger.info(f"输入文本: {text}")
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# 尝试 set_text + send_keys 组合
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try:
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input_elem.set_text(text)
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except:
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pass
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time.sleep(0.5)
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# 检查文本是否输入成功，如果没有，尝试 send_keys
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try:
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current_text = input_elem.get_text()
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if not current_text or current_text != text:
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logger.warning(f"set_text 似乎未生效 (当前: {current_text})，尝试 send_keys...")
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d.send_keys(text)
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except:
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d.send_keys(text)
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# 3. 截图：输入文本后
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take_debug_screenshot(d, "native_03_after_text_input")
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# 尝试发送回车键
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time.sleep(0.5)
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d.press("enter")
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# 尝试点击发送按钮
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try:
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if d(text="发送").exists:
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d(text="发送").click()
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logger.info("已点击 '发送' 按钮 (Native Text)")
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take_debug_screenshot(d, "native_04_after_send_click_text")
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else:
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logger.info("未找到 '发送' 文本控件，尝试图像识别...")
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send_btn_coords = find_send_button(d)
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if send_btn_coords:
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sx, sy = send_btn_coords
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d.click(sx, sy)
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logger.info(f"已点击 '发送' 按钮 (Image Rec): {sx}, {sy}")
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take_debug_screenshot(d, "native_04_after_send_click_image")
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else:
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width, height = d.window_size()
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fallback_x = int(width * 0.9)
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fallback_y = int(height * 0.965)
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logger.info(f"未识别到发送按钮，尝试盲点右下角: {fallback_x}, {fallback_y}")
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d.click(fallback_x, fallback_y)
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take_debug_screenshot(d, "native_04_after_send_click_fallback")
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except Exception as e:
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logger.error(f"点击发送按钮失败: {e}")
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logger.info("输入完成 (Native)")
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native_success = True
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return
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else:
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logger.warning("未找到输入框元素 (Native)，转入坐标点击模式...")
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except Exception as e:
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logger.warning(f"原生控件操作失败，降级为坐标点击: {e}")
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if native_success:
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return
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# 降级方案：使用坐标点击
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if not coords:
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logger.error("坐标无效，无法执行点击输入")
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return
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x, y = coords
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# 坐标安全检查
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if 'd' not in locals():
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d = u2.connect()
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try:
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width, height = d.window_size()
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# 移除过于激进的坐标修正 (95% -> 93% 会导致点到输入框上方)
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# 输入框中心通常在 96%-97% 左右
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if y > height * 0.99:
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logger.warning(f"检测到的输入框坐标 y={y} 过于靠底，修正为 {height * 0.97}")
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y = int(height * 0.97)
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except:
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pass
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try:
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logger.info(f"设备连接成功: {d.info.get('serial')}")
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# 1. 截图：点击输入框前
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take_debug_screenshot(d, "coord_01_before_click_input")
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# 点击输入框
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logger.info(f"点击坐标: ({x}, {y})")
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d.click(x, y)
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time.sleep(0.5)
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d.click(x, y) # Double click
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# 等待键盘弹出
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time.sleep(1.5)
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# 2. 截图：点击输入框后
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take_debug_screenshot(d, "coord_02_after_click_input_keyboard")
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# 输入文本
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logger.info(f"输入文本 (SendKeys): {text}")
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try:
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d.send_keys(text)
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except Exception as sk_e:
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logger.error(f"send_keys 失败: {sk_e}")
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# 3. 截图：输入文本后
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take_debug_screenshot(d, "coord_03_after_input_text")
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time.sleep(0.5)
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d.press("enter")
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# 尝试查找发送按钮并点击
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try:
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if d(text="发送").exists:
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d(text="发送").click()
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logger.info("已点击 '发送' 按钮 (Native Text)")
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take_debug_screenshot(d, "coord_04_after_click_send_native")
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else:
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logger.info("未找到 '发送' 文本控件，尝试图像识别...")
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send_btn_coords = find_send_button(d)
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if send_btn_coords:
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sx, sy = send_btn_coords
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d.click(sx, sy)
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logger.info(f"已点击 '发送' 按钮 (Image Rec): {sx}, {sy}")
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take_debug_screenshot(d, "coord_04_after_click_send_image")
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else:
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width, height = d.window_size()
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fallback_x = int(width * 0.9)
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fallback_y = int(height * 0.965)
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logger.info(f"未识别到发送按钮，尝试盲点右下角: {fallback_x}, {fallback_y}")
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d.click(fallback_x, fallback_y)
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take_debug_screenshot(d, "coord_04_after_click_send_fallback")
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except Exception as e:
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logger.error(f"点击发送按钮失败: {e}")
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logger.info("输入完成 (Coordinate)")
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except Exception as e:
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logger.error(f"自动化操作失败: {e}")
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def analyze_chat_image(image_path, output_path, target_name="对方"):
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"""
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识别微信聊天截图中的头像并画框，识别对话内容
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"""
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logger.info(f"正在分析图片: {image_path}")
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# 读取图片（支持中文路径）
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try:
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img_data = np.fromfile(image_path, dtype=np.uint8)
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img = cv2.imdecode(img_data, cv2.IMREAD_COLOR)
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except Exception as e:
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logger.error(f"读取图片失败: {e}")
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return
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if img is None:
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logger.error("图片读取为空")
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return
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# 备份一份干净的图片用于 OCR (避免识别到画上去的框)
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img_clean = img.copy()
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height, width = img.shape[:2]
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logger.info(f"图片尺寸: {width}x{height}")
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# 调用输入框识别，获取坐标和可视化框
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input_center, input_rect = find_input_box_center(image_path)
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||
# 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()
|