斗地主AI出牌助手--在线调用斗地主AI，实现自动斗地主

简介

程序基于Python3.7开发的斗地主AI出牌助手，目前支持欢乐斗地主桌面版，微信版，也可以自己制作相应其他版本。
此出牌助手核心是识别出三位玩家出牌内容，调用基于DouZero封装的API接口，输入出牌内容，根据AI出牌方案，打出相应的牌。

运行效果

点击查看

核心功能

手牌和位置识别
1. 游戏刚开始根据屏幕位置，截图识别AI玩家手牌及三张底牌
2. 根据玩家手牌判断是否抢地主和加倍
3. 确认三者之间的关系，识别地主和农民角色，确认队友及对手关系
识别每轮三位玩家出牌
1. 根据提示按钮，判断当前的操作
2. 识别三位玩家此轮出的牌，并记录下来
AI出牌方案输出
1. 将出牌记录按格式要求发送给斗地主AI
2. 获取斗地主AI出牌方案，选取最高胜率方案
3. 根据AI出牌方案，选择对相应的牌，并出牌

素材准备

区域定位，获取坐标值
按钮及牌面

核心代码

根据指定窗口句柄截图

def WindowShot(self):
    """
    根据窗口句柄截图
    返回: 图片对象
    """
    windll.user32.SetProcessDPIAware()
    hwnd = self.Handle
    left, top, right, bottom = win32gui.GetClientRect(hwnd)
    w = right - left
    h = bottom - top
    hwnd_dc = win32gui.GetWindowDC(hwnd)
    mfc_dc = win32ui.CreateDCFromHandle(hwnd_dc)
    save_dc = mfc_dc.CreateCompatibleDC()
    bitmap = win32ui.CreateBitmap()
    bitmap.CreateCompatibleBitmap(mfc_dc, w, h)
    save_dc.SelectObject(bitmap)
    # If Special K is running, this number is 3. If not, 1
    result = windll.user32.PrintWindow(hwnd, save_dc.GetSafeHdc(), 3)
    bmpinfo = bitmap.GetInfo()
    bmpstr = bitmap.GetBitmapBits(True)
    img = np.frombuffer(bmpstr, dtype=np.uint8).reshape((bmpinfo["bmHeight"], bmpinfo["bmWidth"], 4))
    img = np.ascontiguousarray(img)[..., :-1]  # make image C_CONTIGUOUS and drop alpha channel
    #img = Image.frombuffer("RGB",(bmpinfo['bmWidth'], bmpinfo['bmHeight']),bmpstr, 'raw', 'BGRX', 0, 1)
    if not result:  # result should be 1
        win32gui.DeleteObject(bitmap.GetHandle())
        save_dc.DeleteDC()
        mfc_dc.DeleteDC()
        win32gui.ReleaseDC(hwnd, hwnd_dc)
        raise RuntimeError(f"Unable to acquire screenshot! Result: {result}")
    #cv2.imwrite('./imgs/print.png', img)
    #return img
    return cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)

在图片中查找第一个相似的图片

def LocateOnImage(self, template, image=None, region=None, confidence=0.9, grayscale=True):
    """
    在image中寻找template，返回第一个查找到的范围

    参数:
    template: 需要查找的图片，文件名或图片对象
    image: 被查找的图片，文件名或图片对象
    region: 查找范围
    confidence: 置信度
    grayscale: 是否为灰度图

    返回值:
    查找到的图片范围
    """
    if image is None:
        image = self.WindowShot()
    return pyautogui.locate(template, image, region=region, confidence=confidence, grayscale=grayscale)

在图片中查找所有相似的图片

def LocateAllOnImage(self, template, image=None, region=None, confidence=0.9, grayscale=True):
    """
    在image中寻找template，返回第一个查找到的范围

    参数:
    template: 需要查找的图片，文件名或图片对象
    image: 被查找的图片，文件名或图片对象
    region: 查找范围
    confidence: 置信度
    grayscale: 是否为灰度图

    返回值:
    查找到的图片范围
    """
    if image is None:
        image = self.WindowShot()
    return pyautogui.locateAll(template, image, region=region, confidence=confidence, grayscale=grayscale)

识别手牌

def GetCards(self, image, player):
    hand_cards = []
    cards = ""
    start_x = 0
    width = player["width"]
    for card in self.AllCardsNC:
        confidence = player["confidence"] or 0.85
        grayscale = True
        if(card in ('D', 'X')):
            confidence = 0.85
            grayscale = False

        card_key = player["prefix"] + card
        matches = self.LocateAllOnImageName(card_key, image, player["region"], confidence, grayscale)

        if len(matches) > 0:
            sorted_matches = sorted(matches, key=lambda match: match[0])
            #print(target_position)
            #print(mark)

            if(card != 'X'):
                start_x = 0

            for match in sorted_matches:
                if(start_x == 0 or start_x + player["width"] < match[0]):
                    #大小王容易判断错误，需要再判断一下颜色值
                    if(card == 'D'):
                        #match = sorted_matches[0]
                        y,x,h,w = match
                        cropped_image = image[x:x+w, y:y+h]
                        #colors = cv2.mean(cropped_image)
                        mean, stddev = cv2.meanStdDev(cropped_image)
                        #print("均值：", mean)
                        #print("差值：", stddev)
                        if(stddev[2][0] > 30):
                            continue
                    start_x = match[0]
                    width = match[2]
                    hand_cards.append({card:match})
                    cards += card
    
    return cards, hand_cards

将出牌记录发送给AI，进行预测

def GetCardsForPredict(self):
    #将出牌记录发送给AI，进行预测
    resultStr = self.PostPredict()
    #获取最高胜率的出牌
    last_move_cards = self.GetPredictWinRates(resultStr)
    return last_move_cards

出牌

def PlayedCards(self, cards, image=None):
    if image is not None:
        window_shot_image = image
    else:
        window_shot_image = self.WindowShot()
    player_self_cards, range = self.GetCards(window_shot_image, self.Players["PlayerSelf"])
    select_index = []
    select_cards = []
    #将cards中的字符顺序反转
    play_cards = cards[::-1]
    cards = play_cards

    for c in cards:
        index = -1
        for card in player_self_cards:
            index += 1
            
            if c == card and index not in select_index:
                select_index.append(index)
                select_cards.append(c)
                #print(range[index])
                cards = cards.replace(c, '', 1)
                card_range = range[index][c]
                break

    cards = play_cards
    index = -1
    #window_shot_image = self.WindowShot()
    for n in select_index:
        window_shot_image = self.WindowShot()
        player_self_cards, range = self.GetCards(window_shot_image, self.Players["PlayerSelf"])
        if(len(player_self_cards) > n):
            #player_self_cards = player_self_cards
            index += 1
            card_range = range[n][cards[index]]
            #如果要出的牌还未选择完毕，则点击
            if(card_range[1] > self.CardTop):
                self.LeftClick(card_range)
        
        time.sleep(0.2)

    return self.GetSelectCards(play_cards)

DouZero 的请求结构体

Predict = {
    "bomb_num":0,#炸弹数量
    "card_play_action_seq":'',#历史出牌动作序列，用逗号分隔
    "last_move_landlord":'',#地主最后出的牌
    "last_move_landlord_down":'',#地主下家最后出的牌
    "last_move_landlord_up":'',#地主上家最后出的牌
    "num_cards_left_landlord":20,#地主手牌剩余数量
    "num_cards_left_landlord_down":17,#地主下家手牌剩余数量
    "num_cards_left_landlord_up":17,#地主上家手牌剩余数量
    "other_hand_cards":'',#还剩余的牌
    "played_cards_landlord":'',#地主所有出的牌
    "played_cards_landlord_down":'',#地主下家所有出的牌
    "played_cards_landlord_up":'',#地主上家所有出的牌
    "player_hand_cards":'',#玩家手中的牌
    "player_position":0,	#-当前玩家的位置序号 0 地主，1 地主下家，2 地主上家
    "three_landlord_cards":''#三张底牌
}

可用的 DouZero 后端地址

快手的 DouZero
https://douzero.org:5000/predict
网易的 PerfectDou
https://outer-perfectdou-demo-gzailab.nie.netease.com:10074/predict
也可根据 RLCard-Showdown 自行搭建本地运行的后端服务

免责声明

本程序仅供娱乐和学习使用，不得用于任何非法用途

参考项目

DouZero：https://github.com/kwai/DouZero
PerfectDou：https://github.com/Netease-Games-AI-Lab-Guangzhou/PerfectDou
rlcard-showdown：https://github.com/datamllab/rlcard-showdown
DouZero_For_HLDDZ_FullAuto: https://github.com/Vincentzyx/DouZero_For_HLDDZ_FullAuto

posted on 2023-11-14 23:56 liongis 阅读(5210) 评论(1) 编辑收藏举报