Python制作安卓游戏外挂

发布时间:2019-12-19 04:41    浏览次数 :

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教你用Python写安卓游戏外挂,python安卓

本次我们选择的安卓游戏对象叫“单词英雄”,大家可以先下载这个游戏。

游戏的界面是这样的:

图片 1

通过选择单词的意思进行攻击,选对了就正常攻击,选错了就象征性的攻击一下。玩了一段时间之后琢磨可以做成自动的,通过PIL识别图片里的单词和选项,然后翻译英文成中文意思,根据中文模糊匹配选择对应的选项。

查找了N多资料以后开始动手,程序用到以下这些东西:

PIL:Python Imaging Library 大名鼎鼎的图片处理模块

pytesser:Python下用来驱动tesseract-ocr来进行识别的模块

Tesseract-OCR:图像识别引擎,用来把图像识别成文字,可以识别英文和中文,以及其它语言

autopy:Python下用来模拟操作鼠标和键盘的模块。

安装步骤(win7环境):

(1)安装PIL,下载地址: Imaging Library 1.1.7 for Python 2.7。

(2)安装pytesser,下载地址:
C:Python27Libsite-packages下,在文件夹下建立pytesser.pth文件,内容为C:Python27Libsite-packagespytesser_v0.0.1

(3)安装Tesseract OCR engine,下载: installer of tesseract-ocr 3.02.02 (including English language data)的安装文件,进行安装。

(4)安装语言包,在 OCR目标下的tessdata文件夹内,用来识别简体中文。

(5)修改C:Python27Libsite-packagespytesser_v0.0.1下的pytesser.py的函数,将原来的image_to_string函数增加语音选择参数language,language='chi_sim'就可以用来识别中文,默认为eng英文。

改好后的pytesser.py:

"""OCR in Python using the Tesseract engine from Google
http://code.google.com/p/pytesser/
by Michael J.T. O'Kelly
V 0.0.1, 3/10/07"""
import Image
import subprocess
import util
import errors
tesseract_exe_name = 'tesseract' # Name of executable to be called at command line
scratch_image_name = "temp.bmp" # This file must be .bmp or other Tesseract-compatible format
scratch_text_name_root = "temp" # Leave out the .txt extension
cleanup_scratch_flag = True # Temporary files cleaned up after OCR operation
def call_tesseract(input_filename, output_filename, language):
 """Calls external tesseract.exe on input file (restrictions on types),
 outputting output_filename+'txt'"""
 args = [tesseract_exe_name, input_filename, output_filename, "-l", language]
 proc = subprocess.Popen(args)
 retcode = proc.wait()
 if retcode!=0:
  errors.check_for_errors()
def image_to_string(im, cleanup = cleanup_scratch_flag, language = "eng"):
 """Converts im to file, applies tesseract, and fetches resulting text.
 If cleanup=True, delete scratch files after operation."""
 try:
  util.image_to_scratch(im, scratch_image_name)
  call_tesseract(scratch_image_name, scratch_text_name_root,language)
  text = util.retrieve_text(scratch_text_name_root)
 finally:
  if cleanup:
   util.perform_cleanup(scratch_image_name, scratch_text_name_root)
 return text
def image_file_to_string(filename, cleanup = cleanup_scratch_flag, graceful_errors=True, language = "eng"):
 """Applies tesseract to filename; or, if image is incompatible and graceful_errors=True,
 converts to compatible format and then applies tesseract. Fetches resulting text.
 If cleanup=True, delete scratch files after operation."""
 try:
  try:
   call_tesseract(filename, scratch_text_name_root, language)
   text = util.retrieve_text(scratch_text_name_root)
  except errors.Tesser_General_Exception:
   if graceful_errors:
    im = Image.open(filename)
    text = image_to_string(im, cleanup)
   else:
    raise
 finally:
  if cleanup:
   util.perform_cleanup(scratch_image_name, scratch_text_name_root)
 return text
if __name__=='__main__':
 im = Image.open('phototest.tif')
 text = image_to_string(im)
 print text
 try:
  text = image_file_to_string('fnord.tif', graceful_errors=False)
 except errors.Tesser_General_Exception, value:
  print "fnord.tif is incompatible filetype. Try graceful_errors=True"
  print value
 text = image_file_to_string('fnord.tif', graceful_errors=True)
 print "fnord.tif contents:", text
 text = image_file_to_string('fonts_test.png', graceful_errors=True)
 print text

(6)安装autopy,下载地址:

说下程序的思路:

1. 首先是通过模拟器在WINDOWS下执行安卓的程序,然后用PicPick进行截图,将战斗画面中需要用到的区域进行测量,记录下具体在屏幕上的位置区域,用图中1来判断战斗是否开始(保存下来用作比对),用2,3,4,5,6的区域抓取识别成文字。

图片 2

计算图片指纹的程序:

def get_hash(self, img):
    #计算图片的hash值
    image = img.convert("L")
    pixels = list(image.getdata())
    avg = sum(pixels) / len(pixels)
    return "".join(map(lambda p : "1" if p > avg else "0", pixels))

图片识别成字符:

#识别出对应位置图像成字符,把字符交给chose处理
  def getWordMeaning(self):
    pic_up = ImageGrab.grab((480,350, 480+300, 350+66))
    pic_aws1 = ImageGrab.grab((463,456, 463+362, 456+45))
    pic_aws2 = ImageGrab.grab((463,530, 463+362, 530+45))
    pic_aws3 = ImageGrab.grab((463,601, 463+362, 601+45))
    pic_aws4 = ImageGrab.grab((463,673, 463+362, 673+45))
    str_up = image_to_string(pic_up).strip().lower()
    #判断当前单词和上次识别单词相同,就不继续识别
    if str_up <> self.lastWord:
      #如果题目单词是英文,选项按中文进行识别
      if str_up.isalpha():
        eng_up = self.dt[str_up].decode('gbk') if self.dt.has_key(str_up) else ''
        chs1 = image_to_string(pic_aws1, language='chi_sim').decode('utf-8').strip()
        chs2 = image_to_string(pic_aws2, language='chi_sim').decode('utf-8').strip()
        chs3 = image_to_string(pic_aws3, language='chi_sim').decode('utf-8').strip()
        chs4 = image_to_string(pic_aws4, language='chi_sim').decode('utf-8').strip()
        print str_up, ':', eng_up
        self.chose(eng_up, (chs1, chs2, chs3, chs4))
      #如果题目单词是中文,选项按英文进行识别
      else:
        chs_up = image_to_string(pic_up, language='chi_sim').decode('utf-8').strip()
        eng1 = image_to_string(pic_aws1).strip()
        eng2 = image_to_string(pic_aws2).strip()
        eng3 = image_to_string(pic_aws3).strip()
        eng4 = image_to_string(pic_aws4).strip()

        e2c1 = self.dt[eng1].decode('gbk') if self.dt.has_key(eng1) else ''
        e2c2 = self.dt[eng2].decode('gbk') if self.dt.has_key(eng2) else ''
        e2c3 = self.dt[eng3].decode('gbk') if self.dt.has_key(eng3) else ''
        e2c4 = self.dt[eng4].decode('gbk') if self.dt.has_key(eng4) else ''
        print chs_up
        self.chose(chs_up, (e2c1, e2c2, e2c3, e2c4))
      self.lastWord = str_up
    return str_up

2. 对于1位置的图片提前截一个保存下来,然后通过计算当前画面和保存下来的图片的距离,判断如果小于40的就表示已经到了选择界面,然后识别2,3,4,5,6成字符,判断如果2位置识别成英文字符的,就用2解析出来的英文在字典中获取中文意思,然后再通过2的中文意思和3,4,5,6文字进行匹配,匹配上汉字最多的就做选择,如果匹配不上默认返回最后一个。之前本来考虑是用Fuzzywuzzy来进行模糊匹配算相似度的,不过后来测试了下对于中文匹配的效果不好,就改成按汉字单个进行匹配计算相似度。

匹配文字进行选择:

#根据传入的题目和选项进行匹配选择
  def chose(self, g, chs_list):
    j, max_score = -1, 0
    same_list = None
    #替换掉题目里的特殊字符
    re_list = [u'~', u',', u'.', u';', u' ', u'a', u'V', u'v', u'i', u'n', u'【', u')', u'_', u'W', u'd', u'j', u'-', u't']
    for i in re_list:
      g = g.replace(i, '')
    print type(g)
    #判断2个字符串中相同字符,相同字符最多的为最佳答案
    for i, chsWord in enumerate(chs_list):
      print type(chsWord)
      l = [x for x in g if x in chsWord and len(x)>0]
      score = len(l) if l else 0

      if score > max_score:
        max_score = score
        j = i
        same_list = l
    #如果没有匹配上默认选最后一个
    if j ==-1:
      print '1. %s; 2. %s; 3. %s; 4. %s; Not found choice.' % (chs_list[0], chs_list[1], chs_list[2], chs_list[3])
    else:
      print '1. %s; 2. %s; 3. %s; 4. %s; choice: %s' % (chs_list[0], chs_list[1], chs_list[2], chs_list[3], chs_list[j])
      for k, v in enumerate(same_list):
        print str(k) + '.' + v,
    order = j + 1
    self.mouseMove(order)
    return order

3.最后通过mouseMove调用autopy操作鼠标点击对应位置进行选择。

程序运行的录像:

程序完成后使用正常,因为图片识别准确率和字典的问题,正确率约为70%左右,效果还是比较满意。程序总体来说比较简单,做出来也就是纯粹娱乐一下,串联使用了图片识别、中文模糊匹配、鼠标模拟操作,算是个简单的小外挂吧,源程序和用到的文件如下:

本次我们选择的安卓游戏对象叫“单词英雄”,大家可以先下载这个游戏。 游戏的界面是这样的:...

最近在玩一款背单词的手机游戏-单词英雄,是一个将背单词和卡牌游戏相结合的游戏,通过选择正确的单词意思进行有效攻击,边玩游戏就把单词给背了。
  游戏的界面是这样的:

图片 3

Paste_Image.png

  通过选择单词的意思进行攻击,选对了就正常攻击,选错了就象征性的攻击一下。玩了一段时间之后琢磨可以做成自动的,通过PIL识别图片里的单词和选项,然后翻译英文成中文意思,根据中文模糊匹配选择对应的选项。
  查找了N多资料以后开始动手,程序用到以下这些东西:
PIL       Python Imaging Library 大名鼎鼎的图片处理模块
pytesser     Python下用来驱动tesseract-ocr来进行识别的模块
Tesseract-OCR  图像识别引擎,用来把图像识别成文字,可以识别英文和中文,以及其它语言
autopy     Python下用来模拟操作鼠标和键盘的模块。

安装步骤(win7环境):
  (1)安装PIL,下载地址:http://www.pythonware.com/products/pil/,安装Python Imaging Library 1.1.7 for Python 2.7。
  (2)安装pytesser,下载地址:http://code.google.com/p/pytesser/,下载解压后直接放在
C:Python27Libsite-packages下,在文件夹下建立pytesser.pth文件,内容为C:Python27Libsite-packagespytesser_v0.0.1
  (3)安装Tesseract OCR engine,下载:https://github.com/tesseract-ocr/tesseract/wiki/Downloads,下载Windows installer of tesseract-ocr 3.02.02 (including English language data)的安装文件,进行安装。
  (4)安装语言包,在https://github.com/tesseract-ocr/tessdata下载chi_sim.traineddata简体中文语言包,放到安装的Tesseract OCR目标下的tessdata文件夹内,用来识别简体中文。
  (5)修改C:Python27Libsite-packagespytesser_v0.0.1下的pytesser.py的函数,将原来的image_to_string函数增加语音选择参数language,language='chi_sim'就可以用来识别中文,默认为eng英文。
改好后的pytesser.py:

"""OCR in Python using the Tesseract engine from Google
http://code.google.com/p/pytesser/
by Michael J.T. O'Kelly
V 0.0.1, 3/10/07"""

import Image
import subprocess
import util
import errors

tesseract_exe_name = 'tesseract' # Name of executable to be called at command line
scratch_image_name = "temp.bmp" # This file must be .bmp or other Tesseract-compatible format
scratch_text_name_root = "temp" # Leave out the .txt extension
cleanup_scratch_flag = True  # Temporary files cleaned up after OCR operation

def call_tesseract(input_filename, output_filename, language):
  """Calls external tesseract.exe on input file (restrictions on types),
  outputting output_filename+'txt'"""
  args = [tesseract_exe_name, input_filename, output_filename, "-l", language]
  proc = subprocess.Popen(args)
  retcode = proc.wait()
  if retcode!=0:
    errors.check_for_errors()

def image_to_string(im, cleanup = cleanup_scratch_flag, language = "eng"):
  """Converts im to file, applies tesseract, and fetches resulting text.
  If cleanup=True, delete scratch files after operation."""
  try:
    util.image_to_scratch(im, scratch_image_name)
    call_tesseract(scratch_image_name, scratch_text_name_root,language)
    text = util.retrieve_text(scratch_text_name_root)
  finally:
    if cleanup:
      util.perform_cleanup(scratch_image_name, scratch_text_name_root)
  return text

def image_file_to_string(filename, cleanup = cleanup_scratch_flag, graceful_errors=True, language = "eng"):
  """Applies tesseract to filename; or, if image is incompatible and graceful_errors=True,
  converts to compatible format and then applies tesseract.  Fetches resulting text.
  If cleanup=True, delete scratch files after operation."""
  try:
    try:
      call_tesseract(filename, scratch_text_name_root, language)
      text = util.retrieve_text(scratch_text_name_root)
    except errors.Tesser_General_Exception:
      if graceful_errors:
        im = Image.open(filename)
        text = image_to_string(im, cleanup)
      else:
        raise
  finally:
    if cleanup:
      util.perform_cleanup(scratch_image_name, scratch_text_name_root)
  return text


if __name__=='__main__':
  im = Image.open('phototest.tif')
  text = image_to_string(im)
  print text
  try:
    text = image_file_to_string('fnord.tif', graceful_errors=False)
  except errors.Tesser_General_Exception, value:
    print "fnord.tif is incompatible filetype.  Try graceful_errors=True"
    print value
  text = image_file_to_string('fnord.tif', graceful_errors=True)
  print "fnord.tif contents:", text
  text = image_file_to_string('fonts_test.png', graceful_errors=True)
  print text

(6)安装autopy,下载地址:https://pypi.python.org/pypi/autopy,下载autopy-0.51.win32-py2.7.exe进行安装,用来模拟鼠标操作。

说下程序的思路:
 1. 首先是通过模拟器在WINDOWS下执行安卓的程序,然后用PicPick进行截图,将战斗画面中需要用到的区域进行测量,记录下具体在屏幕上的位置区域,用图中1来判断战斗是否开始(保存下来用作比对),用2,3,4,5,6的区域抓取识别成文字。

图片 4

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