def TryTencent():
hWin = win32gui.FindWindow("TXGuiFoundation", "腾讯手游助手【标准引擎】")
if hWin:
#t1 = time.clock()
print("搜索到腾讯模拟器......")
l, t, r, b = win32gui.GetWindowRect(hWin)
img = ImageGrab.grab((l, t, r, b))
#img = img.crop((l,t,r,b))
#img.save('C:\\Users\\44780\\Desktop\\t.png', 'png')
#t2 = time.clock()
#print("截取模拟器截图耗时:", t2-t1)
imgList = TryAnalyseImgCD(img)
if (imgList is None or len(imgList) < 2):
print("没有到答题时间或者查不到关键像素")
return
#t3 = time.clock()
#print("图片裁剪耗时:", t3-t2)
question, choices = ocr.ocr_img(imgList)
print('Question: ' + question)
if ('不是' in question) or ('不属于' in question) or (
'不包含' in question) or ('不可能' in question):
print('*****请注意此题为否定题*****')
#t4 = time.clock()
#print("图片识别耗时:", t4-t3)
# 将问题与选项一起搜索方法，并获取搜索到的结果数目
m0 = Thread(methods.run_algorithm(0, question, choices))
m1 = Thread(methods.run_algorithm(1, question, choices))
# 用选项在问题页面中计数出现词频方法
m2 = Thread(methods.run_algorithm(2, question, choices))
m0.start()
m1.start()
m2.start()
def mousedown(event):
global end_x, end_y, ctrlflag
if ctrlflag == 1:
end_x, end_y = event.Position
print(Fore.RED + str(end_x) + ',' + str(end_y) + Fore.RESET)
if start_x < end_x and start_y < end_y:
image = ImageGrab.grab((start_x, start_y, end_x, end_y))
image.save('./screenshot.png', format='PNG')
# image = Image.open("../screenshot.png")
question, choices = ocr.ocr_img_baidu(image, config)
print(choices)
methods.run_algorithm(2, question, choices)
ctrlflag = 0
else:
print(Fore.RED + '请换一种姿势重新选择' + Fore.RESET)
ctrlflag = 0
return True
else:
print('未激活截图模式!')
return True
def game_fun(image_cut):
while True:
pull_screenshot()
#截图参数
image_cut()
q_filePath = "question.png"
c_filePath = "choices.png"
question = question_words(q_filePath, options)
choices = choices_words(c_filePath, options)
#count_base(question, choices)
# 多线程
m1 = Thread(methods.run_algorithm(0, question, choices))
m2 = Thread(methods.run_algorithm(1, question, choices))
m3 = Thread(methods.run_algorithm(2, question, choices))
m1.start()
m2.start()
m3.start()
print('——————————————————————————')
go = input('输入回车继续运行,输入 n 回车结束运行: ')
if go == 'n':
break
img = Image.open("./screenshot.png")
# 文字识别
question, choices = ocr.ocr_img(img)
# t = time.clock()
# 用不同方法输出结果，取消某个方法在前面加上#
# # 打开浏览器方法搜索问题
# methods.run_algorithm(0, question, choices)
# # 将问题与选项一起搜索方法，并获取搜索到的结果数目
# methods.run_algorithm(1, question, choices)
# # 用选项在问题页面中计数出现词频方法
# methods.run_algorithm(2, question, choices)
# 多线程
m1 = Thread(methods.run_algorithm(0, question, choices))
m2 = Thread(methods.run_algorithm(1, question, choices))
m3 = Thread(methods.run_algorithm(2, question, choices))
m1.start()
m2.start()
m3.start()
# end_time = time.clock()
# print(end_time - t)
go = input('输入回车继续运行,输入 n 回车结束运行: ')
if go == 'n':
break
print('------------------------')
import wda
from PIL import Image
from common import ocr, methods
c = wda.Client()
while True:
# 截图
c.screenshot('screenshot.png')
img = Image.open("./screenshot.png")
# 文字识别
question, choices = ocr.ocr_img(img)
# 用不同方法输出结果，取消某个方法在前面加上#
# 打开浏览器方法搜索问题
methods.run_algorithm(0, question, choices)
# 将问题与选项一起搜索方法，并获取搜索到的结果数目
methods.run_algorithm(1, question, choices)
# 用选项在问题页面中计数出现词频方法
methods.run_algorithm(2, question, choices)
go = input('输入回车继续运行,输入 n 回车结束运行: ')
if go == 'n':
break
print('------------------------')
img = Image.open("./screenshot.png")
# 文字识别,可选 Tesseract 和 Baidu ,请在 config/configure.conf 中进行相应配置
# ocr_img: 需要分别截取题目和选项区域，使用 Tesseract
# ocr_img_tess： 题目和选项一起截，使用 Tesseract
# ocr_img_baidu： 题目和选项一起截，使用 baidu ocr，需配置 key
# question, choices = ocr.ocr_img(img, config)
question, choices = ocr.ocr_img_tess(img, config)
# question, choices = ocr.ocr_img_baidu(img, config)
# 用不同方法输出结果，取消某个方法在前面加上#
# 打开浏览器方法搜索问题
methods.run_algorithm(0, question, choices)
# 将问题与选项一起搜索方法，并获取搜索到的结果数目
methods.run_algorithm(1, question, choices)
# 用选项在问题页面中计数出现词频方法
methods.run_algorithm(2, question, choices)
end_time3 = time.clock()
print('用时: {0}'.format(end_time3 - t))
go = input('输入回车继续运行,输入 n 回车结束运行: ')
if go == 'n':
break
print('------------------------')
question, choices = ocr.ocr_img(img)
# t = time.clock()
# 用不同方法输出结果，取消某个方法在前面加上#
# # 打开浏览器方法搜索问题
# methods.run_algorithm(0, question, choices)
# # 将问题与选项一起搜索方法，并获取搜索到的结果数目
# methods.run_algorithm(1, question, choices)
# # 用选项在问题页面中计数出现词频方法
# methods.run_algorithm(2, question, choices)
question = '游的最慢的鱼是哪个'
choices = ['鲸鱼', '旗鱼', '企鹅']
print(question)
print(choices)
# 多线程
# m1 = Thread(methods.run_algorithm(0, question, choices))
m2 = Thread(methods.run_algorithm(1, question, choices))
m3 = Thread(methods.run_algorithm(2, question, choices))
# m1.start()
m2.start()
m3.start()
# end_time = time.clock()
# print(end_time - t)
go = input('输入回车继续运行,输入 n 回车结束运行: ')
if go == 'n':
break
print('------------------------')
choices_img = Image.open("./choices_screenshot.png")
# 文字识别
question, choices = ocr.ocr_img(question_img, choices_img)
print("问题%s \n选择:%s"%(question, choices))
# 用不同方法输出结果，取消某个方法在前面加上#
# 打开浏览器方法搜索问题
# methods.run_algorithm(0, question, choices)
# # 将问题与选项一起搜索方法，并获取搜索到的结果数目
# methods.run_algorithm(1, question, choices)
# # 用选项在问题页面中计数出现词频方法
# methods.run_algorithm(2, question, choices)
Thread(methods.run_algorithm(0, question, choices)).start()
Thread(methods.run_algorithm(1, question, choices)).start()
Thread(methods.run_algorithm(2, question, choices)).start()
# m1.start()
# m2.start()
# m3.start()
go = input('输入回车继续运行,输入 n 回车结束运行: ')
if go == 'n':
break
print('------------------------')
# isTrue = False
