python 自动化操作必备函数功能_python mewtocol协议

#利用下列函数方便实现自动化操作
import os
import pyperclip
import pyautogui
from keyboard import is_pressed
from time import sleep
import cv2

def accRecog(recogImgPath, do=pyautogui.click, method=cv2.TM_CCOEFF_NORMED,debug=0):#精确识别
	'''recogImgPath :需要匹配的小图 如果显示None erorr 路径改为ascii
		do  :pyautogui.moveTo()# 基本移动
				pyautogui.click()  # 左键单击
				pyautogui.doubleClick()  # 左键双击
				pyautogui.rightClick() # 右键单击
				pyautogui.middleClick() # 中键单击
		method:识别算法
				cv2.TM_CCOEFF 最有可能匹配最亮的像素 相关系数
				cv2.TM_CCOEFF_NORMED 通用情况
				cv2.TM_CCORR 最有可能匹配最亮的像素 互相关
				cv2.TM_CCORR_NORMED 通用情况
				cv2.TM_SQDIFF 最有可能匹配最暗的像素
				cv2.TM_SQDIFF_NORMED 通用情况
		debug   :在识别位置画出矩形用于调试
		resize showRect时显示图像的缩放大小'''
	imgBigPath='imgBig.png'
	pyautogui.screenshot(imgBigPath)# 截屏，并保存到本地
	imgBig = cv2.imread(imgBigPath,cv2.IMREAD_GRAYSCALE)# 读入背景图片 cv2.IMREAD_GRAYSCALE：读入灰度图片
	imgSmall = cv2.imread(recogImgPath,cv2.IMREAD_GRAYSCALE)# 读入需要查找的图片
	w, h = imgSmall.shape[::-1]# 得到图片的高和宽
	def rocog(imgBig,imgSmall,method):#以method算法返回imgSmall在imgBigGray的左上位置x,y
		res = cv2.matchTemplate(imgBig,imgSmall,method)# 模板匹配操作 应用模板算法，返回一系列指标
		# 注意 TM_SQDIFF 或者 TM_SQDIFF_NORMED 算法使用最小值为最优
		min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)# 从res中挑选最优指标 得到最大和最小值得位置
		if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:top_left = min_loc#左上角的位置
		else:top_left = max_loc
		y = top_left[1]
		x = top_left[0]
		return x,y
		print(top_left,bottom_right)

	if not debug:
		x,y=rocog(imgBig,imgSmall,method)
		x=int(x+w//2)
		y=int(y+h//2)
		print(x,y)
		do(x,y)
	else:
		from matplotlib import pyplot as plt
		methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR','cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
		imgBigColor = cv2.imread(imgBigPath,cv2.IMREAD_COLOR) #cv2.IMREAD_COLOR：默认参数，读入一副彩色图片，忽略alpha通道
		imgBigColor = cv2.cvtColor(imgBigColor, cv2.COLOR_BGR2RGB) # 颜色转换
		plt.rcParams['figure.figsize']=(60,40)#6000x4000
		for i,meth in enumerate(methods):# 依次使用算法匹配
			#img = img2.copy()
			method = eval(meth)
			x,y=rocog(imgBig,imgSmall,method)
			cv2.rectangle(imgBigColor,(x,y), (x+w,y+h), (0,0,255), 2) # 画出矩形框 rgb, 线宽
			plt.axis('off') # 关闭坐标
			plt.subplot(3, 2, i+1)#增加子图
			plt.imshow(imgBigColor)#显示图片
			plt.title(meth)#图标题
			plt.xticks([])
			plt.yticks([])
		plt.suptitle(f"Algorithm Comparison")#主标题
		plt.savefig(f"AlgorithmComparison.png")
		plt.show()
	os.remove(imgBigPath)
def imgDetect(ifile):#检测图片
	confidence=confidenceCeiling
	while True:
		pos=pyautogui.locateCenterOnScreen(ifile,confidence=confidence)#识别 confidence识别精度0-1  pos=x,y
		if pos:return pos
		confidence-=0.02
		if confidence<=confidenceFloor:
			print(f'未检测到{ifile}')
			break
def tillClick(*ifiles):#直到识别出才点击后停止 ifile 如果引起错误 多个fileNotfound图片先把路径改为英文字符
	confidence=confidenceCeiling
	while True:
		breakout=0
		for ifile in ifiles:
			pos=pyautogui.locateCenterOnScreen(ifile,confidence=confidence)#识别 confidence识别精度0-1  pos=x,y
			if pos:
				pyautogui.click(pos)
				breakout=1
				break
		if breakout:break#jump out loop
		confidence-=0.01
		if confidence<=confidenceFloor:
			print(f'无法识别{ifile}正在重试')
			confidence=confidenceCeiling
def tillHotkey(ifile,*hotkeys):#直到识别出才热键后停止 ifile 如果引起fileNotfound错误先把路径改为英文字符
	confidence=confidenceCeiling
	while True:
		pos=pyautogui.locateCenterOnScreen(ifile,confidence=confidence)#识别 confidence识别精度0-1  pos=x,y
		if pos:
			pyautogui.hotkey(*hotkeys)#快捷键
			break
		confidence-=0.01
		if confidence<=confidenceFloor:
			print(f'无法识别{ifile}正在重试')
			confidence=confidenceCeiling
def inputText(text):#输入文字
	pyperclip.copy(text)#复制
	pyautogui.hotkey('ctrl','v')#粘贴
def enterText(text):#输入文字并确认
	inputText(text)
	pyautogui.press('enter')#确认
def scroll(d):#滚动 -d向上 d=1=↓x1
	pyautogui.scroll(-d*75)
def typeChar(char,count):
	pyautogui.typewrite([char for n in range(count)])#按照列表中的每个键名 进行按键 sec为按键间隔
def turnTo(url):#转到该网页
	tillHotkey(r'edge\address.jpg','alt','d')
	enterText(url)
def openE():#打开egde
	pyautogui.hotkey('shift','alt','s')#快捷键
	enterText('microsoft edge')
def chooseFile(path):#文件选择对话框
	tillClick(r'openFile.jpg')
	enterText(path)
def openFile(path):#listary打开列表中第一个文件
	pyautogui.hotkey('shift','alt','s')
	enterText(path)
def getText(path):#获取文本
	with open(path,'r',encoding='utf-8') as f:
		text=f.read()
	return text
def getF(file,lv):#返回指定迭代目录级 0为原目录 1为basename 2为dirname(file)+basename etc
	f=[]
	def recF(file,lv):
		if lv:
			file=os.path.dirname(file)
			lv-=1
			recF(file,lv)
			f.append(file)
	if lv:
		recF(file,lv)
		return file.replace(min(f)+'\\','')
	elif not lv:return file

confidenceCeiling=0.8#图片识别精度上限 过高识别不出 过低会错误识别
confidenceFloor=0.5#图片识别精度下限 过高识别不出 过低会错误识别 建议直接使用 accRecog
fontStyle='C:/Users/TRIX/AppData/Local/Microsoft/Windows/Fonts/HarmonyOS_Sans_SC_Bold.ttf'
pyautogui.FAILSAFE=True#鼠标移动到屏幕左上角停止程序
pyautogui.PAUSE=0.3# pyautogui指令执行间隔 间隔太小执行过快会无法正常响应

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