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HuangHai
215
Test/T1_StartWeiXin.py
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215
Test/T1_StartWeiXin.py
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# 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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# 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 analyze_chat_image(image_path, output_path):
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"""
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识别微信聊天截图中的头像并画框
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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
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# 注意：cv2.imread 不支持中文路径，需要用 np.fromfile 读取
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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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height, width = img.shape[:2]
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logger.info(f"图片尺寸: {width}x{height}")
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# 1. 预处理
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# 转为灰度图
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 使用自适应阈值二值化
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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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# 形态学操作：闭运算，填充内部空洞
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
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closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
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# 2. 轮廓查找
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contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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logger.info(f"检测到轮廓数量: {len(contours)}")
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avatar_count = 0
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# 3. 筛选轮廓
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for contour in contours:
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# 获取外接矩形
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x, y, w, h = cv2.boundingRect(contour)
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# 筛选条件优化：
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# 1. 形状接近正方形 (放宽宽高比限制: 0.8 ~ 1.2)
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aspect_ratio = float(w) / h
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# 2. 尺寸适中
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# 假设头像宽度在屏幕宽度的 6% 到 15% 之间
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min_w = width * 0.06
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max_w = width * 0.15
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# 3. 位置筛选
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# 排除底部输入框区域 (假设底部 10% 为输入区域)
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if y > height * 0.9:
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continue
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# 左侧头像：靠左边 (x < width * 0.18)
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# 右侧头像：靠右边 (x > width * 0.82)
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is_left = x < width * 0.18
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is_right = x > width * 0.82
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if 0.8 <= aspect_ratio <= 1.2 and min_w < w < max_w:
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if is_left or is_right:
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# 确定颜色
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# 左侧：蓝色 (BGR: 255, 0, 0)
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# 右侧：黄色 (BGR: 0, 255, 255)
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color = (255, 0, 0) if is_left else (0, 255, 255)
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# 绘制矩形框，线宽为 3
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cv2.rectangle(img, (x, y), (x + w, y + h), color, 3)
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label = "Left" if is_left else "Right"
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avatar_count += 1
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logger.info(f"找到头像: 位置=({x},{y}), 尺寸={w}x{h}, 侧别={label}")
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logger.info(f"共标记了 {avatar_count} 个头像")
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# 4. 保存结果
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try:
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# cv2.imwrite 不支持中文路径，使用 imencode + tofile
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ext = os.path.splitext(output_path)[1]
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cv2.imencode(ext, img)[1].tofile(output_path)
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logger.info(f"✅ 分析结果已保存至: {output_path}")
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except Exception as e:
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logger.error(f"保存分析图片失败: {e}")
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def main():
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logger.info("开始执行微信搜索测试...")
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# 连接设备
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try:
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d = u2.connect()
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logger.info(f"设备连接成功: {d.info.get('serial')}")
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except Exception as e:
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logger.error(f"设备连接失败: {e}")
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return
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# 1. 启动微信
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logger.info("步骤 1: 启动微信...")
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d.app_start("com.tencent.mm", stop=True)
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# 等待微信启动完成
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time.sleep(5)
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# 获取屏幕尺寸
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w, h = d.window_size()
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logger.info(f"屏幕尺寸: {w}x{h}")
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# 2. 点击搜索按钮 (参考 Opener.py 的坐标比例: w * 0.84, h * 0.08)
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search_x = int(w * 0.84)
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search_y = int(h * 0.08)
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logger.info(f"步骤 2: 点击搜索按钮 (坐标: {search_x}, {search_y})...")
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d.click(search_x, search_y)
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time.sleep(2)
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# 3. 输入 "糖豆爸爸"
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target_name = "糖豆爸爸"
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logger.info(f"步骤 3: 输入搜索内容 '{target_name}'...")
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try:
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# 启用 FastInputIME 以支持中文输入
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d.set_input_ime(True)
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# 点击搜索框获取焦点 (参考 Opener.py: w * 0.4, h * 0.08)
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d.click(int(w * 0.4), int(h * 0.08))
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time.sleep(1)
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# 输入文字
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d.send_keys(target_name)
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time.sleep(2)
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# 恢复输入法
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d.set_input_ime(False)
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except Exception as e:
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logger.error(f"输入文字失败: {e}")
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try:
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d(focused=True).set_text(target_name)
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except:
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pass
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# 4. 点击搜索结果
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logger.info("步骤 4: 查找并点击搜索结果...")
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time.sleep(2)
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found = False
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# 策略 A: 精确匹配文本
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if d(text=target_name).exists:
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logger.info(f"找到文本为 '{target_name}' 的元素，点击...")
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d(text=target_name).click()
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found = True
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else:
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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...")
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# 策略 B: 坐标点击 (参考 Opener.py 点击第一个结果: w * 0.5, h * 0.18)
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result_x = int(w * 0.5)
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result_y = int(h * 0.18)
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logger.info(f"尝试点击第一个搜索结果位置 ({result_x}, {result_y})...")
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d.click(result_x, result_y)
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found = True
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if found:
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logger.info("✅ 已执行点击操作，应该已进入对话框。")
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# 5. 截图保存结果
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logger.info("步骤 5: 截图并分析...")
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# 等待界面加载稳定
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time.sleep(3)
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# 创建截图目录
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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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# 生成文件名
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timestamp = time.strftime("%Y%m%d_%H%M%S")
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filename = f"chat_result_{timestamp}.jpg"
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save_path = os.path.join(screenshot_dir, filename)
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try:
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d.screenshot(save_path)
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logger.info(f"✅ 原始截图已保存: {save_path}")
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# 6. 分析截图并标记头像
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logger.info("步骤 6: 自动标记头像...")
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analyzed_filename = f"chat_result_{timestamp}_analyzed.jpg"
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analyzed_path = os.path.join(screenshot_dir, analyzed_filename)
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analyze_chat_image(save_path, analyzed_path)
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except Exception as e:
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logger.error(f"❌ 截图或分析失败: {e}")
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else:
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logger.error("❌ 未能定位到搜索结果，跳过截图。")
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logger.info("测试结束。")
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if __name__ == "__main__":
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main()
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