undefined

undefined

import pyautogui
import time

x = max(0, min(x, max_x - 1))
y = max(0, min(y, max_y - 1))

pyautogui.click(x, y)

undefined

"""
自动化 UI 测试示例
测试一个假设的登录页面
"""
import pyautogui
import time

def test_login_flow():
    # 1. 截取初始状态
    initial_screenshot = pyautogui.screenshot()
    initial_screenshot.save("test_01_initial.png")
    
    # 2. 查找并点击登录按钮
    button_location = pyautogui.locateOnScreen(
        "login_button.png",
        confidence=0.9
    )
    if button_location:
        center = pyautogui.center(button_location)
        pyautogui.click(center.x, center.y)
        time.sleep(1)
    
    # 3. 输入用户名
    pyautogui.typewrite("testuser@example.com", interval=0.01)
    pyautogui.press('tab')
    
    # 4. 输入密码
    pyautogui.typewrite("TestPassword123", interval=0.01)
    
    # 5. 点击提交
    pyautogui.press('return')
    time.sleep(2)
    
    # 6. 验证结果
    result_screenshot = pyautogui.screenshot()
    result_screenshot.save("test_02_result.png")
    
    # 检查是否出现成功提示
    success_indicator = pyautogui.locateOnScreen(
        "success_message.png",
        confidence=0.8
    )
    
    if success_indicator:
        print("✅ 测试通过：登录成功")
        return True
    else:
        print("❌ 测试失败：未找到成功提示")
        return False

undefined

"""
数据录入自动化示例
将 Excel 数据自动填入网页表单
"""
import pyautogui
import pandas as pd
import time

def automate_data_entry(excel_file, form_template):
    """
    从 Excel 读取数据并自动填入表单
    
    Args:
        excel_file: Excel 文件路径
        form_template: 表单字段与 Excel 列的映射
    """
    # 1. 读取 Excel 数据
    df = pd.read_excel(excel_file)
    print(f"读取到 {len(df)} 条记录")
    
    # 2. 遍历每条记录
    for index, row in df.iterrows():
        print(f"\n正在处理第 {index + 1} 条记录...")
        
        # 3. 填写每个字段
        for field_name, column_name in form_template.items():
            value = row.get(column_name, '')
            
            # 查找表单字段（需要提前准备字段截图）
            field_location = pyautogui.locateOnScreen(
                f"form_field_{field_name}.png",
                confidence=0.8
            )
            
            if field_location:
                # 点击字段
                center = pyautogui.center(field_location)
                pyautogui.click(center.x, center.y)
                time.sleep(0.2)
                
                # 输入值
                pyautogui.hotkey('ctrl', 'a')  # 全选
                pyautogui.typewrite(str(value), interval=0.01)
                time.sleep(0.2)
            else:
                print(f"  ⚠️ 未找到字段: {field_name}")
        
        # 4. 提交表单
        submit_btn = pyautogui.locateOnScreen(
            "submit_button.png",
            confidence=0.8
        )
        if submit_btn:
            center = pyautogui.center(submit_btn)
            pyautogui.click(center.x, center.y)
            print("  ✅ 已提交")
            time.sleep(2)  # 等待提交完成
        else:
            print("  ⚠️ 未找到提交按钮")
        
        # 5. 准备下一条记录
        # 可能需要点击"添加新记录"或返回列表
        time.sleep(1)
    
    print("\n🎉 所有记录处理完成！")

automate_data_entry("data.xlsx", form_template)

undefined

"""
屏幕监控与告警示例
监控特定区域变化，发现变化时发送通知
"""
import pyautogui
import cv2
import numpy as np
import time
from datetime import datetime

def monitor_screen_region(region, template_image=None, check_interval=5, callback=None):
    """
    监控屏幕特定区域的变化
    
    Args:
        region: (left, top, width, height) 监控区域
        template_image: 要查找的模板图像路径（可选）
        check_interval: 检查间隔（秒）
        callback: 发现变化时的回调函数
    
    Returns:
        监控会话对象（可调用 stop() 停止）
    """
    class MonitorSession:
        def __init__(self):
            self.running = True
            self.baseline = None
        
        def stop(self):
            self.running = False
    
    session = MonitorSession()
    
    print(f"🔍 开始监控区域: {region}")
    print(f"⏱️  检查间隔: {check_interval}秒")
    print("按 Ctrl+C 停止监控\n")
    
    try:
        while session.running:
            # 捕获当前区域
            current = pyautogui.screenshot(region=region)
            current_array = np.array(current)
            
            if template_image:
                # 模式1: 查找模板图像
                template_location = pyautogui.locateOnScreen(
                    template_image,
                    confidence=0.8
                )
                
                if template_location:
                    print(f"✅ [{datetime.now()}] 找到模板图像: {template_location}")
                    if callback:
                        callback('template_found', {
                            'location': template_location,
                            'screenshot': current
                        })
            else:
                # 模式2: 检测变化
                if session.baseline is None:
                    session.baseline = current_array
                    print(f"📸 [{datetime.now()}] 已建立基准图像")
                else:
                    # 计算差异
                    diff = cv2.absdiff(session.baseline, current_array)
                    diff_gray = cv2.cvtColor(diff, cv2.COLOR_RGB2GRAY)
                    diff_score = np.mean(diff_gray)
                    
                    if diff_score > 10:  # 阈值可调
                        print(f"⚠️  [{datetime.now()}] 检测到变化! 差异分数: {diff_score:.2f}")
                        if callback:
                            callback('change_detected', {
                                'diff_score': diff_score,
                                'screenshot': current,
                                'baseline': session.baseline
                            })
                        # 更新基准
                        session.baseline = current_array
            
            time.sleep(check_interval)
    
    except KeyboardInterrupt:
        print("\n🛑 监控已停止")
    
    return session

# 10分钟后停止（实际使用可以一直运行）
# time.sleep(600)
# monitor.stop()

# 示例2: 查找特定图像
# monitor = monitor_screen_region(
#     region=(0, 0, 1920, 1080),
#     template_image="target_button.png",  # 要查找的图像
#     check_interval=2,
#     callback=alert_callback
# )

undefined

import pyautogui

def get_all_screen_sizes():
    """获取所有显示器尺寸（仅 Windows 支持多显示器详细信息）"""
    # macOS 返回主屏尺寸
    # Windows 可以使用 pygetwindow 或 win32api 获取多显示器信息
    
    primary = pyautogui.size()
    print(f"主屏幕尺寸: {primary}")
    
    # Windows 示例（需要安装 pywin32）
    try:
        import win32api
        monitors = win32api.EnumDisplayMonitors()
        for i, monitor in enumerate(monitors):
            print(f"显示器 {i+1}: {monitor[2]}")
    except ImportError:
        pass
    
    return primary

def screenshot_specific_monitor(monitor_num=0):
    """截图指定显示器（实验性功能）"""
    # 目前 pyautogui 主要支持主显示器
    # 多显示器支持需要平台特定代码
    pass

import cv2
import numpy as np
import pyautogui
import time
from functools import lru_cache

class ScreenCache:
    """屏幕缓存优化器"""
    
    def __init__(self, cache_duration=0.5):
        self.cache_duration = cache_duration
        self.last_capture = None
        self.last_capture_time = 0
    
    def get_screenshot(self, region=None):
        """获取截图（带缓存）"""
        current_time = time.time()
        
        # 检查缓存是否有效
        if (self.last_capture is not None and 
            current_time - self.last_capture_time < self.cache_duration and
            region is None):
            return self.last_capture
        
        # 捕获新截图
        screenshot = pyautogui.screenshot(region=region)
        
        if region is None:
            self.last_capture = screenshot
            self.last_capture_time = current_time
        
        return screenshot
    
    def clear_cache(self):
        """清除缓存"""
        self.last_capture = None
        self.last_capture_time = 0

class FastImageFinder:
    """快速图像查找器（使用多尺度金字塔）"""
    
    def __init__(self, scales=[0.8, 0.9, 1.0, 1.1, 1.2]):
        self.scales = scales
    
    def find_multi_scale(self, template_path, screenshot=None, confidence=0.8):
        """
        多尺度图像查找
        
        Returns:
            (x, y, scale) 或 None
        """
        if screenshot is None:
            screenshot = pyautogui.screenshot()
        
        template = cv2.imread(template_path)
        if template is None:
            return None
        
        screenshot_cv = cv2.cvtColor(np.array(screenshot), cv2.COLOR_RGB2BGR)
        
        for scale in self.scales:
            # 缩放模板
            scaled_template = cv2.resize(
                template,
                None,
                fx=scale,
                fy=scale,
                interpolation=cv2.INTER_AREA
            )
            
            # 模板匹配
            result = cv2.matchTemplate(
                screenshot_cv,
                scaled_template,
                cv2.TM_CCOEFF_NORMED
            )
            
            _, max_val, _, max_loc = cv2.minMaxLoc(result)
            
            if max_val >= confidence:
                h, w = scaled_template.shape[:2]
                center_x = max_loc[0] + w // 2
                center_y = max_loc[1] + h // 2
                return (center_x, center_y, scale)
        
        return None

undefined

"""
安全最佳实践
"""

import pyautogui
import hashlib
import time

class SecureAutomation:
    """安全自动化包装器"""
    
    def __init__(self):
        self.action_log = []
        self.max_retries = 3
        self.rate_limit_delay = 0.1  # 操作间隔
    
    def log_action(self, action, details):
        """记录操作日志"""
        timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
        log_entry = {
            'timestamp': timestamp,
            'action': action,
            'details': details,
            'hash': hashlib.md5(f"{timestamp}{action}{details}".encode()).hexdigest()[:8]
        }
        self.action_log.append(log_entry)
    
    def safe_click(self, x, y, description=""):
        """安全点击（带验证）"""
        try:
            # 验证坐标在屏幕范围内
            screen_width, screen_height = pyautogui.size()
            if not (0 <= x < screen_width and 0 <= y < screen_height):
                raise ValueError(f"坐标 ({x}, {y}) 超出屏幕范围")
            
            # 执行点击
            pyautogui.moveTo(x, y, duration=0.2)
            time.sleep(self.rate_limit_delay)
            pyautogui.click()
            
            # 记录日志
            self.log_action('click', f"({x}, {y}) - {description}")
            
            return True
            
        except Exception as e:
            self.log_action('click_failed', f"({x}, {y}) - Error: {str(e)}")
            return False
    
    def safe_typewrite(self, text, interval=0.01):
        """安全输入（敏感信息不记录）"""
        try:
            pyautogui.typewrite(text, interval=interval)
            self.log_action('typewrite', f"输入 {len(text)} 个字符 [内容已隐藏]")
            return True
        except Exception as e:
            self.log_action('typewrite_failed', f"Error: {str(e)}")
            return False
    
    def get_action_report(self):
        """生成操作报告"""
        total = len(self.action_log)
        successful = sum(1 for log in self.action_log if 'failed' not in log['action'])
        failed = total - successful
        
        report = f"""
=== 自动化操作报告 ===
总操作数: {total}
成功: {successful}
失败: {failed}
成功率: {(successful/total*100):.1f}%

详细日志:
"""
        for log in self.action_log:
            report += f"[{log['timestamp']}] [{log['hash']}] {log['action']}: {log['details']}\n"
        
        return report

undefined

import pyautogui

undefined

import pyautogui
import cv2
import numpy as np

undefined

undefined

Args:
    user_request: 用户的自然语言请求
"""
# 1. AI 解析用户意图
intent = parse_intent(user_request)

if intent == 'screenshot':
    # 2. 执行截图
    screenshot = pyautogui.screenshot()
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    path = f"screenshot_{timestamp}.png"
    screenshot.save(path)
    return f"已截图并保存到: {path}"

elif intent == 'click_button':
    # 2. 查找并点击按钮
    button_name = extract_button_name(user_request)
    location = pyautogui.locateOnScreen(f"{button_name}.png")
    if location:
        pyautogui.click(pyautogui.center(location))
        return f"已点击按钮: {button_name}"
    else:
        return f"未找到按钮: {button_name}"

# ... 其他意图处理

undefined

undefined

steps:
- uses: actions/checkout@v3

- name: Set up Python
  uses: actions/setup-python@v4
  with:
    python-version: '3.11'

- name: Install dependencies
  run: |
    pip install pyautogui opencv-python-headless numpy Pillow

- name: Run visual tests
  run: python tests/visual_regression.py

- name: Upload screenshots
  uses: actions/upload-artifact@v3
  with:
    name: screenshots
    path: screenshots/

undefined

undefined

while True:
    # Capture screen
    current = pyautogui.screenshot()
    current_array = np.array(current)
    
    if baseline is not None:
        # Calculate change
        diff = cv2.absdiff(baseline, current_array)
        diff_score = np.mean(diff)
        screen_change_gauge.set(diff_score)
    
    baseline = current_array
    
    # Check for template
    try:
        location = pyautogui.locateOnScreen('alert_icon.png', confidence=0.8)
        if location:
            template_match_gauge.set(1.0)
        else:
            template_match_gauge.set(0.0)
    except:
        template_match_gauge.set(0.0)
    
    time.sleep(5)

undefined