272 lines
9.5 KiB
Python
272 lines
9.5 KiB
Python
# coding=utf-8
|
||
import uiautomator2 as u2
|
||
import time
|
||
import logging
|
||
import sys
|
||
import os
|
||
import cv2
|
||
import numpy as np
|
||
|
||
# 配置日志
|
||
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
||
logger = logging.getLogger("TestWeChat")
|
||
|
||
def analyze_chat_image(image_path, output_path):
|
||
"""
|
||
识别微信聊天截图中的头像并画框
|
||
"""
|
||
logger.info(f"正在分析图片: {image_path}")
|
||
|
||
# 读取图片
|
||
# 注意：cv2.imread 不支持中文路径，需要用 np.fromfile 读取
|
||
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
|
||
|
||
height, width = img.shape[:2]
|
||
logger.info(f"图片尺寸: {width}x{height}")
|
||
|
||
# 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)}")
|
||
|
||
# 4. 绘制对话内容框 (Green/Red Boxes)
|
||
# 策略：按顺序遍历头像，如果发现同侧连续，则视为一组。
|
||
# 从当前组的第一个头像上方开始，直到下一个不同侧的头像上方（或底部）。
|
||
|
||
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
|
||
|
||
# 当前组范围: avatars[i] ... avatars[j-1]
|
||
# 确定绘制区域的 Y 轴范围
|
||
|
||
# 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: 下一组第一个头像的上方 (减去较大间距，例如 -30px)，或者当前组最后一个头像的底部加上边距
|
||
# 为了让框之间有明显间隔，我们采取策略：
|
||
# 如果有下一组，End Y = 下一组第一个头像的 y - 30 (留出间隙)
|
||
# 如果没有下一组，End Y = 屏幕底部区域上方
|
||
|
||
if j < len(avatars):
|
||
end_y = max(start_y + 10, avatars[j]['y'] - 30)
|
||
else:
|
||
end_y = int(height * 0.9) # 到底部输入框上方
|
||
|
||
# 绘制大框
|
||
# 左侧 (Left) -> 对方 -> 绿色 (0, 255, 0)
|
||
# 右侧 (Right) -> 我 -> 红色 (0, 0, 255)
|
||
# 注意 OpenCV 颜色是 BGR
|
||
box_color = (0, 255, 0) if current_side == "Left" else (0, 0, 255)
|
||
|
||
# 绘制矩形 (空心，线宽 5)
|
||
# X 轴范围：0 到 width
|
||
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}")
|
||
|
||
# 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']
|
||
# 左侧：蓝色 (BGR: 255, 0, 0)
|
||
# 右侧：黄色 (BGR: 0, 255, 255)
|
||
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']}")
|
||
|
||
logger.info(f"共标记了 {len(avatars)} 个头像")
|
||
|
||
# 6. 保存结果
|
||
try:
|
||
# 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}")
|
||
|
||
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...")
|
||
analyzed_filename = f"chat_result_{timestamp}_analyzed.jpg"
|
||
analyzed_path = os.path.join(screenshot_dir, analyzed_filename)
|
||
|
||
analyze_chat_image(save_path, analyzed_path)
|
||
|
||
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()
|