基于深度学习目标检测的人工智能玩游戏

前言

1. 利用目标检测算法实现对游戏界面目标位置的获取
2. PyKeyboard、ctypes实现鼠标键盘的控制
3. 实现特定操作

一、目标检测算法

吧游戏界面截图下来，对特定目标进行Labelimg标注，训练一个目标检测算法模型

二、操作游戏流程

1.获取显示屏界面图像

def grab_screen(region=None):

    hwin = win32gui.GetDesktopWindow()

    if region:
        left, top, x2, y2 = region
        width = x2 - left + 1
        height = y2 - top + 1
    else:
        width = win32api.GetSystemMetrics(win32con.SM_CXVIRTUALSCREEN)
        height = win32api.GetSystemMetrics(win32con.SM_CYVIRTUALSCREEN)
        left = win32api.GetSystemMetrics(win32con.SM_XVIRTUALSCREEN)
        top = win32api.GetSystemMetrics(win32con.SM_YVIRTUALSCREEN)


    hwindc = win32gui.GetWindowDC(hwin)
    srcdc = win32ui.CreateDCFromHandle(hwindc)
    memdc = srcdc.CreateCompatibleDC()
    bmp = win32ui.CreateBitmap()
    bmp.CreateCompatibleBitmap(srcdc, width, height)
    memdc.SelectObject(bmp)
    memdc.BitBlt((0, 0), (width, height), srcdc, (left, top), win32con.SRCCOPY)

    signedIntsArray = bmp.GetBitmapBits(True)
    img = np.fromstring(signedIntsArray, dtype='uint8')
    img.shape = (height, width, 4)

    srcdc.DeleteDC()
    memdc.DeleteDC()
    win32gui.ReleaseDC(hwin, hwindc)
    win32gui.DeleteObject(bmp.GetHandle())

    return cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)

2.获取游戏窗口界面图像

def get_screan():
    # hwnd = win32gui.FindWindow(None, 'C:\Windows\system32\cmd.exe')
    app = QApplication(sys.argv)
    hwnd = win32gui.FindWindow(None, 'window') # window 窗口名称
    # win32gui.SetForegroundWindow(hwnd)
    rect = win32gui.GetWindowRect(hwnd)
    # print('aaaaaaa', hwnd)
    screen = QApplication.primaryScreen()
    img = screen.grabWindow(hwnd).toImage()
    # img.save("screenshot.jpg")
    arr = convertQImageToMat(img)
    return cv2.cvtColor(arr, cv2.COLOR_BGRA2BGR), rect

3.鼠标键盘操作

试了很多种方法，这种方法是能成功对游戏界面进行鼠标键盘操作的

def mouse_click(x, y, n=1):
    # x, y鼠标在窗口中的坐标
    # win32api.SetCursorPos([x, y])
    ctypes.windll.user32.SetCursorPos(int(x), int(y))
    for i in range(n):
        # win32api.mouse_event(win32con.MOUSEEVENTF_MOVE, x, y)
        win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, 0, 0, 0, 0)
        win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, 0, 0, 0, 0)

三、验证码识别

数字字母验证码识别，准确率很好，但是需要32位的python来调用E语言的库

class Ver_code_2:
    def __init__(self, path):
        # 载入识别库
        self.dll = windll.LoadLibrary(path + r"\OCRS.dll")
        # 载入字库与建立字库索引
        with open(os.path.join(path, r"zimushuziku.cnn"), "rb") as file:
            # 载入字库
            self.word_bank = file.read()
            # 建立字库索引
            self.word_index = self.dll.INIT(path, self.word_bank, len(self.word_bank), -1, 1)

    def ocr(self, image):
        Str = create_string_buffer(100)  # 创建文本缓冲区
        self.dll.OCR(self.word_index, image, len(image), Str)  # 利用DLL中的识别函数进行识别
        # print(Str)
        return Str.raw.decode("utf-8").rstrip('\x00')  # 对识别的返回值进行编码后返回，这里的\x00是删除缓冲区的空白符

总结

识别依赖于目标检测算法的准确率，操作过程中的逻辑处理需严密