python 控制鼠标键盘库 pyautogui

1. 鼠标相关

pyautogui.moveTo(100, 200)   # moves mouse to X of 100, Y of 200.
pyautogui.moveRel(0, 50)     # move the mouse down 50 pixels.
 pyautogui.click()  # click the mouse
pyautogui.click(button='right')  # right-click the mouse
 pyautogui.click(clicks=2)  # double-click the left mouse button
pyautogui.doubleClick()  # perform a left-button double click
pyautogui.scroll(10)   # scroll up 10 "clicks"

2. 键盘相关

#模拟输入信息
pyautogui.typewrite(message='Hello world!',interval=0.5)
pyautogui.press('enter')  # press the Enter key
# the hotkey() can be passed several key strings which will be pressed down in order, and then released in reverse order. This code:
pyautogui.hotkey('ctrl', 'v')

3.截图

im = pyautogui.screenshot(region=(0,0, 300, 400))

4. 跟图片进行像素比较

    def check_target_img(self, regin, target_img_name):
        '''
        如果区域跟目标图片大部分一样，return true, other return false
        :param regin:
        :param target_img_name:
        :return:
        '''
        img = pyautogui.screenshot(region=regin)
        pixdata = img.load()
        w, h = img.size

        no_code = Image.open(target_img_name)
        no_code_pixdata = no_code.load()

        diff_pix_count = 0
        for y in range(h):
            for x in range(w):
                if pixdata[x, y] != no_code_pixdata[x, y]:
                    diff_pix_count += 1
        # print(diff_pix_count)
        if diff_pix_count < 10:
            return True
        else:
            return False

5. 粘贴板相关

import pyperclip 
   def get_txt_from_board(self):
        '''
         从剪贴板得到文字
        :return:
        '''
        return pyperclip.paste()

    def set_text_to_board(self, text):
        """设置剪贴板文本"""
        pyperclip.copy(text)

6.得到图片在给点图片中的位置

    def find_img_pos_in_regin(self, img_name, regin):
        '''
        :param img_name: 要查找的图片名字
        :param regin:  查找区域
        :return:  图片在中心位置
        '''
        import cv2
        import numpy as np

        im = pyautogui.screenshot(region=regin).convert('RGB')
        open_cv_image = np.array(im)
        # 加载原始RGB图像
        # img_rgb = cv2.imread("brow_locate.png")

        img_rgb = open_cv_image[:, :, ::-1].copy()
        # 创建一个原始图像的灰度版本，所有操作在灰度版本中处理，然后在RGB图像中使用相同坐标还原
        img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)

        # 加载将要搜索的图像模板
        template = cv2.imread(img_name, 0)
        # 记录图像模板的尺寸
        w, h = template.shape[::-1]

        # 使用matchTemplate对原始灰度图像和图像模板进行匹配
        res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
        # 设定阈值
        threshold = 0.7
        # res大于70%
        loc = np.where(res >= threshold)
        # 使用灰度图像中的坐标对原始RGB图像进行标记
        for pt in zip(*loc[::-1]):
            print(pt)
            return (pt[0] + w / 2, pt[1] + h / 2)

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