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Vincentzyx 2021-07-28 19:47:43 +08:00
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.gitattributes vendored Normal file
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# Auto detect text files and perform LF normalization
* text=auto

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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/

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# Default ignored files
/shelf/
/workspace.xml

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.idea/DouZero_For_HappyDouDiZhu-master.iml Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$" />
<orderEntry type="jdk" jdkName="Python 3.8" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
<component name="PyDocumentationSettings">
<option name="format" value="PLAIN" />
<option name="myDocStringFormat" value="Plain" />
</component>
</module>

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<component name="InspectionProjectProfileManager">
<profile version="1.0">
<option name="myName" value="Project Default" />
<inspection_tool class="DuplicatedCode" enabled="true" level="WEAK WARNING" enabled_by_default="true">
<Languages>
<language minSize="78" name="Python" />
</Languages>
</inspection_tool>
<inspection_tool class="PyPep8Inspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
<option name="ignoredErrors">
<list>
<option value="E501" />
<option value="E302" />
<option value="E303" />
</list>
</option>
</inspection_tool>
<inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
<option name="ignoredErrors">
<list>
<option value="N803" />
<option value="N806" />
<option value="N802" />
<option value="N801" />
</list>
</option>
</inspection_tool>
<inspection_tool class="SpellCheckingInspection" enabled="false" level="TYPO" enabled_by_default="false">
<option name="processCode" value="true" />
<option name="processLiterals" value="true" />
<option name="processComments" value="true" />
</inspection_tool>
<inspection_tool class="SqlNoDataSourceInspection" enabled="false" level="WARNING" enabled_by_default="false" />
</profile>
</component>

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<component name="InspectionProjectProfileManager">
<settings>
<option name="USE_PROJECT_PROFILE" value="false" />
<version value="1.0" />
</settings>
</component>

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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8" project-jdk-type="Python SDK" />
</project>

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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/DouZero_For_HappyDouDiZhu-master.iml" filepath="$PROJECT_DIR$/.idea/DouZero_For_HappyDouDiZhu-master.iml" />
</modules>
</component>
</project>

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BidHelper.py Normal file
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# -*- coding: utf-8 -*-
# Created by: Vincentzyx
from douzero.env.game import GameEnv
from douzero.evaluation.deep_agent import DeepAgent
RealCard2EnvCard = {'3': 3, '4': 4, '5': 5, '6': 6, '7': 7,
'8': 8, '9': 9, 'T': 10, 'J': 11, 'Q': 12,
'K': 13, 'A': 14, '2': 17, 'X': 20, 'D': 30}
card_play_model_path_dict = {
'landlord': "baselines/douzero_WP/landlord.ckpt",
'landlord_up': "baselines/douzero_WP/landlord_up.ckpt",
'landlord_down': "baselines/douzero_WP/landlord_down.ckpt"
}
user_position = "landlord" # 玩家角色代码：0-地主上家, 1-地主, 2-地主下家
ai_players = [0, 0]
ai_players[0] = user_position
ai_players[1] = DeepAgent(user_position, card_play_model_path_dict[user_position])
env = GameEnv(ai_players)
card_play_data_list = {}
def GetWinRate(cards):
env.reset()
card_play_data_list.update({
'three_landlord_cards': [RealCard2EnvCard[i] for i in "333"],
'landlord': [RealCard2EnvCard[i] for i in cards],
'landlord_up': [RealCard2EnvCard[i] for i in "33333333333333333"],
'landlord_down': [RealCard2EnvCard[i] for i in "33333333333333333"]
})
env.card_play_init(card_play_data_list)
action_message = env.step(user_position)
win_rate = float(action_message["win_rate"].replace("%",""))
return win_rate

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BidModel.py Normal file
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# -*- coding: utf-8 -*-
# Created by: Vincentzyx
import os
import torch
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset
import time
def EnvToOnehot(cards):
Env2IdxMap = {3:0,4:1,5:2,6:3,7:4,8:5,9:6,10:7,11:8,12:9,13:10,14:11,17:12,20:13,30:14}
cards = [Env2IdxMap[i] for i in cards]
Onehot = torch.zeros((4,15))
for i in range(0, 15):
Onehot[:cards.count(i),i] = 1
return Onehot
def RealToOnehot(cards):
RealCard2EnvCard = {'3': 0, '4': 1, '5': 2, '6': 3, '7': 4,
'8': 5, '9': 6, 'T': 7, 'J': 8, 'Q': 9,
'K': 10, 'A': 11, '2': 12, 'X': 13, 'D': 14}
cards = [RealCard2EnvCard[c] for c in cards]
Onehot = torch.zeros((4,15))
for i in range(0, 15):
Onehot[:cards.count(i),i] = 1
return Onehot
class Net(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(60, 512)
self.fc2 = nn.Linear(512, 512)
self.fc3 = nn.Linear(512, 512)
self.fc4 = nn.Linear(512, 512)
self.fc5 = nn.Linear(512, 512)
self.fc6 = nn.Linear(512, 1)
self.dropout5 = nn.Dropout(0.5)
self.dropout3 = nn.Dropout(0.3)
self.dropout1 = nn.Dropout(0.1)
def forward(self, input):
x = self.fc1(input)
x = torch.relu(self.dropout1(self.fc2(x)))
x = torch.relu(self.dropout3(self.fc3(x)))
x = torch.relu(self.dropout5(self.fc4(x)))
x = torch.relu(self.dropout5(self.fc5(x)))
x = self.fc6(x)
return x
UseGPU = False
device = torch.device('cuda:0')
net = Net()
net.eval()
if UseGPU:
net = net.to(device)
if os.path.exists("bid_weights.pkl"):
net.load_state_dict(torch.load('bid_weights.pkl'))
def predict(cards):
input = RealToOnehot(cards)
if UseGPU:
input = input.to(device)
input = torch.flatten(input)
win_rate = net(input)
return win_rate[0].item() * 100

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FarmerModel.py Normal file
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# -*- coding: utf-8 -*-
# Created by: Vincentzyx
import os
import torch
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset
import time
def EnvToOnehot(cards):
Env2IdxMap = {3:0,4:1,5:2,6:3,7:4,8:5,9:6,10:7,11:8,12:9,13:10,14:11,17:12,20:13,30:14}
cards = [Env2IdxMap[i] for i in cards]
Onehot = torch.zeros((4,15))
for i in range(0, 15):
Onehot[:cards.count(i),i] = 1
return Onehot
def RealToOnehot(cards, llc):
RealCard2EnvCard = {'3': 0, '4': 1, '5': 2, '6': 3, '7': 4,
'8': 5, '9': 6, 'T': 7, 'J': 8, 'Q': 9,
'K': 10, 'A': 11, '2': 12, 'X': 13, 'D': 14}
cards = [RealCard2EnvCard[c] for c in cards]
llcs = [RealCard2EnvCard[c] for c in llc]
Onehot = torch.zeros((7,15))
for i in range(0, 15):
Onehot[:cards.count(i),i] = 1
Onehot[4:llcs.count(i)+4,i] = 1
return Onehot
class Net(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(105, 512)
self.fc2 = nn.Linear(512, 512)
self.fc3 = nn.Linear(512, 512)
self.fc4 = nn.Linear(512, 512)
self.fc5 = nn.Linear(512, 512)
self.fc6 = nn.Linear(512, 1)
self.dropout5 = nn.Dropout(0.5)
self.dropout3 = nn.Dropout(0.3)
self.dropout1 = nn.Dropout(0.1)
def forward(self, input):
x = self.fc1(input)
x = torch.relu(self.dropout3(self.fc2(x)))
x = torch.relu(self.dropout5(self.fc3(x)))
x = torch.relu(self.dropout5(self.fc4(x)))
x = torch.relu(self.dropout5(self.fc5(x)))
x = self.fc6(x)
x = torch.sigmoid(x)
return x
Nets = {"up": Net(), "down": Net()}
if os.path.exists("landlord_up_weights.pkl"):
Nets["up"].load_state_dict(torch.load("landlord_up_weights.pkl"))
Nets["up"].eval()
if os.path.exists("landlord_down_weights.pkl"):
Nets["down"].load_state_dict(torch.load("landlord_down_weights.pkl"))
Nets["down"].eval()
def predict(cards, llc, type="up"):
net = Nets[type]
x = torch.flatten(RealToOnehot(cards, llc))
y = net(x)[0].item()
return y * 100

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GameHelper.py Normal file
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# -*- coding: utf-8 -*-
# Created by: Vincentzyx
import win32gui
import win32ui
import win32api
from ctypes import windll
from PIL import Image
import cv2
import pyautogui
import matplotlib.pyplot as plt
import numpy as np
import os
import time
import threading
from win32con import WM_LBUTTONDOWN, MK_LBUTTON, WM_LBUTTONUP, WM_MOUSEMOVE
import multiprocessing as mp
from PyQt5 import QtGui, QtWidgets, QtCore
from PyQt5.QtCore import QTime, QEventLoop
Pics = {}
ReqQueue = mp.Queue()
ResultQueue = mp.Queue()
Processes = []
def GetSingleCardQueue(reqQ, resQ, Pics):
while True:
while not reqQ.empty():
image, i, sx, sy, sw, sh, checkSelect = reqQ.get()
result = GetSingleCard(image, i, sx, sy, sw, sh, checkSelect, Pics)
del image
if result is not None:
resQ.put(result)
time.sleep(0.01)
def ShowImg(image):
plt.imshow(image)
plt.show()
def DrawRectWithText(image, rect, text):
img = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
x, y, w, h = rect
img2 = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
img2 = cv2.putText(img2, text, (x, y + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
return Image.fromarray(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB))
def CompareCard(card):
order = {"3": 0, "4": 1, "5": 2, "6": 3, "7": 4, "8": 5, "9": 6, "T": 7, "J": 8, "Q": 9, "K": 10, "A": 11, "2": 12,
"X": 13, "D": 14}
return order[card]
def CompareCardInfo(card):
order = {"3": 0, "4": 1, "5": 2, "6": 3, "7": 4, "8": 5, "9": 6, "T": 7, "J": 8, "Q": 9, "K": 10, "A": 11, "2": 12,
"X": 13, "D": 14}
return order[card[0]]
def CompareCards(cards1, cards2):
if len(cards1) != len(cards2):
return False
cards1.sort(key=CompareCard)
cards2.sort(key=CompareCard)
for i in range(0, len(cards1)):
if cards1[i] != cards2[i]:
return False
return True
def GetListDifference(l1, l2):
temp1 = []
temp1.extend(l1)
temp2 = []
temp2.extend(l2)
for i in l2:
if i in temp1:
temp1.remove(i)
for i in l1:
if i in temp2:
temp2.remove(i)
return temp1, temp2
def FindImage(fromImage, template, threshold=0.9):
w, h, _ = template.shape
fromImage = cv2.cvtColor(np.asarray(fromImage), cv2.COLOR_RGB2BGR)
res = cv2.matchTemplate(fromImage, template, cv2.TM_CCOEFF_NORMED)
loc = np.where(res >= threshold)
points = []
for pt in zip(*loc[::-1]):
points.append(pt)
return points
def GetSingleCard(image, i, sx, sy, sw, sh, checkSelect, Pics):
cardSearchFrom = 0
AllCardsNC = ['rD', 'bX', '2', 'A', 'K', 'Q', 'J', 'T', '9', '8', '7', '6', '5', '4', '3']
currCard = ""
ci = cardSearchFrom
while ci < len(AllCardsNC):
if "r" in AllCardsNC[ci] or "b" in AllCardsNC[ci]:
result = pyautogui.locate(needleImage=Pics["m" + AllCardsNC[ci]], haystackImage=image,
region=(sx + 50 * i, sy - checkSelect * 25, sw, sh), confidence=0.9)
if result is not None:
cardPos = (sx + 50 * i + sw // 2, sy - checkSelect * 25 + sh // 2)
cardSearchFrom = ci
currCard = AllCardsNC[ci][1]
cardInfo = (currCard, cardPos)
return cardInfo
break
else:
outerBreak = False
for card_type in ["r", "b"]:
result = pyautogui.locate(needleImage=Pics["m" + card_type + AllCardsNC[ci]],
haystackImage=image,
region=(sx + 50 * i, sy - checkSelect * 25, sw, sh), confidence=0.9)
if result is not None:
cardPos = (sx + 50 * i + sw // 2, sy - checkSelect * 25 + sh // 2)
cardSearchFrom = ci
currCard = AllCardsNC[ci]
cardInfo = (currCard, cardPos)
outerBreak = True
return cardInfo
break
if outerBreak:
break
if ci == len(AllCardsNC) - 1 and checkSelect == 0:
checkSelect = 1
ci = cardSearchFrom - 1
ci += 1
return None
def RunThreads():
for file in os.listdir("pics"):
info = file.split(".")
if info[1] == "png":
tmpImage = Image.open("pics/" + file)
Pics.update({info[0]: tmpImage})
for ti in range(20):
p = mp.Process(target=GetSingleCardQueue, args=(ReqQueue, ResultQueue, Pics))
p.start()
def LocateOnImage(image, template, region=None, confidence=0.9):
if region is not None:
x, y, w, h = region
imgShape = image.shape
image = image[y:y+h, x:x+w,:]
res = cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED)
if (res >= confidence).any():
return True
else:
return None
class GameHelper:
def __init__(self):
self.ScreenZoomRate = 1.25
self.Pics = {}
self.PicsCV = {}
self.Handle = win32gui.FindWindow("Hlddz", None)
self.Interrupt = False
for file in os.listdir("pics"):
info = file.split(".")
if info[1] == "png":
tmpImage = Image.open("pics/" + file)
imgCv = cv2.imread("pics/" + file)
self.Pics.update({info[0]: tmpImage})
self.PicsCV.update({info[0]: imgCv})
def Screenshot(self, region=None): # -> (im, (left, top))
hwnd = self.Handle
# im = Image.open(r"C:\Users\q9294\Desktop\llc.png")
# im = im.resize((1796, 1047))
# return im, (0,0)
left, top, right, bot = win32gui.GetWindowRect(hwnd)
width = right - left
height = bot - top
width = int(width / self.ScreenZoomRate)
height = int(height / self.ScreenZoomRate)
hwndDC = win32gui.GetWindowDC(hwnd)
mfcDC = win32ui.CreateDCFromHandle(hwndDC)
saveDC = mfcDC.CreateCompatibleDC()
saveBitMap = win32ui.CreateBitmap()
saveBitMap.CreateCompatibleBitmap(mfcDC, width, height)
saveDC.SelectObject(saveBitMap)
result = windll.user32.PrintWindow(hwnd, saveDC.GetSafeHdc(), 0)
bmpinfo = saveBitMap.GetInfo()
bmpstr = saveBitMap.GetBitmapBits(True)
im = Image.frombuffer(
"RGB",
(bmpinfo['bmWidth'], bmpinfo['bmHeight']),
bmpstr, 'raw', 'BGRX', 0, 1)
win32gui.DeleteObject(saveBitMap.GetHandle())
saveDC.DeleteDC()
mfcDC.DeleteDC()
win32gui.ReleaseDC(hwnd, hwndDC)
im = im.resize((1796, 1047))
if region is not None:
im = im.crop((region[0], region[1], region[0] + region[2], region[1] + region[3]))
if result:
return im, (left, top)
else:
return None, (0, 0)
def LocateOnScreen(self, templateName, region, confidence=0.9):
image, _ = self.Screenshot()
return pyautogui.locate(needleImage=self.Pics[templateName],
haystackImage=image, region=region, confidence=confidence)
def ClickOnImage(self, templateName, region=None, confidence=0.9):
image, _ = self.Screenshot()
result = pyautogui.locate(needleImage=self.Pics[templateName], haystackImage=image, confidence=confidence, region=region)
if result is not None:
self.LeftClick((result[0],result[1]))
def GetCardsState(self, image):
st = time.time()
states = []
cardStartPos = pyautogui.locate(needleImage=self.Pics["card_edge"], haystackImage=image,
region=(313, 747, 1144, 200), confidence=0.85)
if cardStartPos is None:
return []
sx = cardStartPos[0] + 10
cardSearchFrom = 0
sy, sw, sh = 770, 50, 55
for i in range(0, 20):
haveWhite = pyautogui.locate(needleImage=self.Pics["card_white"], haystackImage=image,
region=(sx + 50 * i, sy, 50, 50), confidence=0.8)
if haveWhite is not None:
break
result = pyautogui.locate(needleImage=self.Pics["card_upper_edge"], haystackImage=image,
region=(sx + 50 * i, 720, sw, 38), confidence=0.9)
checkSelect = 0
if result is not None:
result = pyautogui.locate(needleImage=self.Pics['card_overlap'], haystackImage=image,
region=(sx + 50 * i, 750, sw, 38), confidence=0.85)
if result is None:
checkSelect = 1
states.append(checkSelect)
print("GetStates Costs ", time.time()-st)
return states
def GetCardsMulti(self, image):
st = time.time()
cardStartPos = pyautogui.locate(needleImage=self.Pics["card_edge"], haystackImage=image,
region=(313, 747, 1144, 200), confidence=0.85)
if cardStartPos is None:
return [],[]
sx = cardStartPos[0] + 10
AllCardsNC = ['rD', 'bX', '2', 'A', 'K', 'Q', 'J', 'T', '9', '8', '7', '6', '5', '4', '3']
hand_cards = []
select_map = []
cardSearchFrom = 0
sy, sw, sh = 770, 50, 55
for i in range(0, 20):
haveWhite = pyautogui.locate(needleImage=self.Pics["card_white"], haystackImage=image,
region=(sx + 50 * i, sy, 60, 60), confidence=0.8)
if haveWhite is not None:
break
result = pyautogui.locate(needleImage=self.Pics["card_upper_edge"], haystackImage=image,
region=(sx + 50 * i, 720, sw, 50), confidence=0.9)
checkSelect = 0
if result is not None:
result = pyautogui.locate(needleImage=self.Pics['card_overlap'], haystackImage=image,
region=(sx + 50 * i, 750, sw, 50), confidence=0.85)
if result is None:
checkSelect = 1
select_map.append(checkSelect)
ReqQueue.put((image, i, sx, sy, sw, sh, checkSelect))
QtWidgets.QApplication.processEvents(QEventLoop.AllEvents, 10)
st = time.time()
while len(hand_cards) != len(select_map):
while not ResultQueue.empty():
hand_cards.append(ResultQueue.get())
time.sleep(0.01)
QtWidgets.QApplication.processEvents(QEventLoop.AllEvents, 10)
hand_cards.sort(key=CompareCardInfo, reverse=True)
print("GetCardsMP Costs ", time.time()-st)
return hand_cards, select_map
def GetCards(self, image):
st = time.time()
imgCv = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
cardStartPos = pyautogui.locate(needleImage=self.Pics["card_edge"], haystackImage=image,
region=(313, 747, 1144, 200), confidence=0.85)
if cardStartPos is None:
return [],[]
sx = cardStartPos[0] + 10
AllCardsNC = ['rD', 'bX', '2', 'A', 'K', 'Q', 'J', 'T', '9', '8', '7', '6', '5', '4', '3']
hand_cards = []
select_map = []
cardSearchFrom = 0
sy, sw, sh = 770, 50, 55
for i in range(0, 20):
haveWhite = pyautogui.locate(needleImage=self.Pics["card_white"], haystackImage=image,
region=(sx + 50 * i, sy, 60, 60), confidence=0.8)
if haveWhite is not None:
break
result = pyautogui.locate(needleImage=self.Pics["card_upper_edge"], haystackImage=image,
region=(sx + 50 * i, 720, sw, 50), confidence=0.9)
checkSelect = 0
if result is not None:
result = pyautogui.locate(needleImage=self.Pics['card_overlap'], haystackImage=image,
region=(sx + 50 * i, 750, sw, 50), confidence=0.85)
if result is None:
checkSelect = 1
select_map.append(checkSelect)
currCard = ""
ci = cardSearchFrom
while ci < len(AllCardsNC):
if "r" in AllCardsNC[ci] or "b" in AllCardsNC[ci]:
result = LocateOnImage(imgCv, self.PicsCV["m" + AllCardsNC[ci]], region=(sx + 50 * i, sy - checkSelect * 25, sw, sh), confidence=0.91)
# result = pyautogui.locate(needleImage=self.Pics["m" + AllCardsNC[ci]], haystackImage=image,
# region=(sx + 50 * i, sy - checkSelect * 25, sw, sh), confidence=0.9)
if result is not None:
cardPos = (sx + 50 * i + sw // 2, sy - checkSelect * 25 + sh // 2)
cardSearchFrom = ci
currCard = AllCardsNC[ci][1]
cardInfo = (currCard, cardPos)
hand_cards.append(cardInfo)
else:
outerBreak = False
for card_type in ["r", "b"]:
result = LocateOnImage(imgCv, self.PicsCV["m" + card_type + AllCardsNC[ci]], region=(sx + 50 * i, sy - checkSelect * 25, sw, sh), confidence=0.91)
# result = pyautogui.locate(needleImage=self.Pics["m" + card_type + AllCardsNC[ci]],
# haystackImage=image,
# region=(sx + 50 * i, sy - checkSelect * 25, sw, sh), confidence=0.9)
if result is not None:
cardPos = (sx + 50 * i + sw // 2, sy - checkSelect * 25 + sh // 2)
cardSearchFrom = ci
currCard = AllCardsNC[ci]
cardInfo = (currCard, cardPos)
hand_cards.append(cardInfo)
outerBreak = True
break
if outerBreak:
break
if ci == len(AllCardsNC) - 1 and checkSelect == 0:
checkSelect = 1
ci = cardSearchFrom - 1
ci += 1
QtWidgets.QApplication.processEvents(QEventLoop.AllEvents, 10)
print("GetCards Costs ", time.time()-st)
return hand_cards, select_map
def LeftClick(self, pos):
x, y = pos
lParam = win32api.MAKELONG(x, y)
win32gui.PostMessage(self.Handle, WM_MOUSEMOVE, MK_LBUTTON, lParam)
win32gui.PostMessage(self.Handle, WM_LBUTTONDOWN, MK_LBUTTON, lParam)
win32gui.PostMessage(self.Handle, WM_LBUTTONUP, MK_LBUTTON, lParam)
def SelectCards(self, cards):
cards = [card for card in cards]
tobeSelected = []
tobeSelected.extend(cards)
image, windowPos = self.Screenshot()
handCardsInfo, states = self.GetCards(image)
cardSelectMap = []
for card in handCardsInfo:
c = card[0]
if c in tobeSelected:
cardSelectMap.append(1)
tobeSelected.remove(c)
else:
cardSelectMap.append(0)
clickMap = []
handcards = [c[0] for c in handCardsInfo]
for i in range(0, len(cardSelectMap)):
if cardSelectMap[i] == states[i]:
clickMap.append(0)
else:
clickMap.append(1)
while 1 in clickMap:
for i in range(0, len(clickMap)):
if clickMap[i] == 1:
self.LeftClick(handCardsInfo[i][1])
break
time.sleep(0.1)
if self.Interrupt:
break
image, _ = self.Screenshot()
states = self.GetCardsState(image)
clickMap = []
for i in range(0, len(cardSelectMap)):
if cardSelectMap[i] == states[i]:
clickMap.append(0)
else:
clickMap.append(1)
QtWidgets.QApplication.processEvents(QEventLoop.AllEvents, 10)
# for file in os.listdir("pics"):
# info = file.split(".")
# if info[1] == "png":
# tmpImage = Image.open("pics/" + file)
# imgBGR = cv2.imread("pics/" + file)
# Pics.update({info[0]: tmpImage})
if __name__ == "__main__":
mp.freeze_support()
class A:
def __init__(self):
pass
Pics = {}
PicsCV = {}
Handle = win32gui.FindWindow("Hlddz", None)
form = A()
form.MyHandCardsPos = (250, 764, 1141, 70) # 我的截图区域
form.LPlayedCardsPos = (463, 355, 380, 250) # 左边截图区域
form.RPlayedCardsPos = (946, 355, 380, 250) # 右边截图区域
form.LandlordFlagPos = [(1281, 276, 110, 140), (267, 695, 110, 140), (424, 237, 110, 140)] # 地主标志截图区域(右-我-左)
form.ThreeLandlordCardsPos = (753, 32, 287, 136) # 地主底牌截图区域，resize成349x168
form.PassBtnPoss = (686, 659, 419, 100)
GameHelper = GameHelper()
# img, _ = GameHelper.Screenshot()
img = Image.open(r"C:\Users\q9294\Desktop\cardselect.png")
img2 = Image.open(r"pics/card_corner.png")
img = img.resize((1796, 1047))
# imgcv = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
# st = time.time()
# re = LocateOnImage(imgcv, GameHelper.PicsCV["card_edge"], region=None, confidence=0.999)
# print(re)
# print(time.time()-st)
# st = time.time()
# re = pyautogui.locate(needleImage=GameHelper.Pics["card_edge"], haystackImage=img, confidence=0.9)
# print(re)
# print(time.time()-st)
# st = time.time()
img, _ = GameHelper.Screenshot()
cards, _= GameHelper.GetCards(img)
# cards = "".join([i[0] for i in cards])
print(cards)
print(len(cards))
# et = time.time()
# print(et - st)
# pos2 = pyautogui.locate(needleImage=Pics["card_edge"], haystackImage=img, confidence=0.9)
# pos = FindImage(img, PicsCV["card_corner"], threshold=0.7)
# print(pos)
# print(pos2)
# for p in pos:
# img = DrawRectWithText(img, (p[0], p[1], 50, 50), "p1")
# img = DrawRectWithText(img, (pos2[0], pos2[1], 50, 50), "p2")
# img = DrawRectWithText(img, (sx+50*i, sy-checkSelect*25,sw,sh), c)
# img = DrawRectWithText(img, form.LPlayedCardsPos, "LPlayed")
# img = DrawRectWithText(img, form.RPlayedCardsPos, "RPlayed")
# img = DrawRectWithText(img, form.MyHandCardsPos, "MyCard")
# img = DrawRectWithText(img, form.ThreeLandlordCardsPos, "ThreeLLCPos")
# img = DrawRectWithText(img, form.LandlordFlagPos[0], "RFlag")
# img = DrawRectWithText(img, form.LandlordFlagPos[1], "MyFlag")
# img = DrawRectWithText(img, form.LandlordFlagPos[2], "LFlag")
# img = DrawRectWithText(img, form.PassBtnPoss, "Btns")
# ShowImg(img)
exit()

201
LICENSE Normal file
View File

@ -0,0 +1,201 @@
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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64
LandlordModel.py Normal file
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# -*- coding: utf-8 -*-
# Created by: Vincentzyx
import os
import torch
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset
import time
def EnvToOnehot(cards):
Env2IdxMap = {3:0,4:1,5:2,6:3,7:4,8:5,9:6,10:7,11:8,12:9,13:10,14:11,17:12,20:13,30:14}
cards = [Env2IdxMap[i] for i in cards]
Onehot = torch.zeros((4,15))
for i in range(0, 15):
Onehot[:cards.count(i),i] = 1
return Onehot
def RealToOnehot(cards):
RealCard2EnvCard = {'3': 0, '4': 1, '5': 2, '6': 3, '7': 4,
'8': 5, '9': 6, 'T': 7, 'J': 8, 'Q': 9,
'K': 10, 'A': 11, '2': 12, 'X': 13, 'D': 14}
cards = [RealCard2EnvCard[c] for c in cards]
Onehot = torch.zeros((4,15))
for i in range(0, 15):
Onehot[:cards.count(i),i] = 1
return Onehot
class Net(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(60, 512)
self.fc2 = nn.Linear(512, 512)
self.fc3 = nn.Linear(512, 512)
self.fc4 = nn.Linear(512, 512)
self.fc5 = nn.Linear(512, 512)
self.fc6 = nn.Linear(512, 1)
self.dropout5 = nn.Dropout(0.5)
self.dropout3 = nn.Dropout(0.3)
self.dropout1 = nn.Dropout(0.1)
def forward(self, input):
x = self.fc1(input)
x = torch.relu(self.dropout3(self.fc2(x)))
x = torch.relu(self.dropout5(self.fc3(x)))
x = torch.relu(self.dropout5(self.fc4(x)))
x = torch.relu(self.dropout5(self.fc5(x)))
x = self.fc6(x)
return x
net = Net()
net.eval()
if os.path.exists("landlord_weights.pkl"):
net.load_state_dict(torch.load('landlord_weights.pkl'))
else:
print("landlord_weights.pkl not found")
def predict(cards):
cards_onehot = torch.flatten(RealToOnehot(cards))
y_predict = net(cards_onehot)
return y_predict[0].item() * 100

217
MainWindow.py Normal file
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# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'MainWindow.ui'
#
# Created by: PyQt5 UI code generator 5.15.4
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this file unless you know what you are doing.
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Form(object):
def setupUi(self, Form):
Form.setObjectName("Form")
Form.resize(703, 421)
font = QtGui.QFont()
font.setFamily("Arial")
font.setPointSize(9)
font.setBold(True)
font.setItalic(False)
font.setWeight(75)
Form.setFont(font)
Form.setWindowOpacity(0.8)
self.WinRate = QtWidgets.QLabel(Form)
self.WinRate.setGeometry(QtCore.QRect(480, 150, 201, 61))
font = QtGui.QFont()
font.setFamily("等线")
font.setPointSize(14)
font.setBold(False)
font.setItalic(False)
font.setWeight(50)
self.WinRate.setFont(font)
self.WinRate.setAlignment(QtCore.Qt.AlignCenter)
self.WinRate.setObjectName("WinRate")
self.InitCard = QtWidgets.QPushButton(Form)
self.InitCard.setGeometry(QtCore.QRect(80, 360, 121, 41))
font = QtGui.QFont()
font.setFamily("等线")
font.setPointSize(14)
font.setBold(False)
font.setItalic(False)
font.setWeight(50)
self.InitCard.setFont(font)
self.InitCard.setStyleSheet("")
self.InitCard.setObjectName("InitCard")
self.UserHandCards = QtWidgets.QLabel(Form)
self.UserHandCards.setGeometry(QtCore.QRect(40, 160, 421, 41))
font = QtGui.QFont()
font.setFamily("等线")
font.setPointSize(14)
font.setBold(False)
font.setItalic(False)
font.setWeight(50)
self.UserHandCards.setFont(font)
self.UserHandCards.setAlignment(QtCore.Qt.AlignCenter)
self.UserHandCards.setObjectName("UserHandCards")
self.LPlayer = QtWidgets.QFrame(Form)
self.LPlayer.setGeometry(QtCore.QRect(20, 80, 201, 61))
font = QtGui.QFont()
font.setFamily("等线")
font.setPointSize(9)
font.setBold(False)
font.setItalic(False)
font.setWeight(50)
self.LPlayer.setFont(font)
self.LPlayer.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.LPlayer.setFrameShadow(QtWidgets.QFrame.Raised)
self.LPlayer.setObjectName("LPlayer")
self.LPlayedCard = QtWidgets.QLabel(self.LPlayer)
self.LPlayedCard.setGeometry(QtCore.QRect(0, 0, 201, 61))
font = QtGui.QFont()
font.setFamily("等线")
font.setPointSize(14)
font.setBold(False)
font.setItalic(False)
font.setWeight(50)
self.LPlayedCard.setFont(font)
self.LPlayedCard.setAlignment(QtCore.Qt.AlignCenter)
self.LPlayedCard.setObjectName("LPlayedCard")
self.RPlayer = QtWidgets.QFrame(Form)
self.RPlayer.setGeometry(QtCore.QRect(250, 80, 201, 61))
font = QtGui.QFont()
font.setFamily("等线")
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setWeight(50)
self.RPlayer.setFont(font)
self.RPlayer.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.RPlayer.setFrameShadow(QtWidgets.QFrame.Raised)
self.RPlayer.setObjectName("RPlayer")
self.RPlayedCard = QtWidgets.QLabel(self.RPlayer)
self.RPlayedCard.setGeometry(QtCore.QRect(0, 0, 201, 61))
font = QtGui.QFont()
font.setFamily("等线")
font.setPointSize(14)
font.setBold(False)
font.setItalic(False)
font.setWeight(50)
self.RPlayedCard.setFont(font)
self.RPlayedCard.setAlignment(QtCore.Qt.AlignCenter)
self.RPlayedCard.setObjectName("RPlayedCard")
self.Player = QtWidgets.QFrame(Form)
self.Player.setGeometry(QtCore.QRect(480, 80, 201, 61))
font = QtGui.QFont()
font.setFamily("等线")
font.setPointSize(9)
font.setBold(False)
font.setItalic(False)
font.setWeight(50)
self.Player.setFont(font)
self.Player.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.Player.setFrameShadow(QtWidgets.QFrame.Raised)
self.Player.setObjectName("Player")
self.PredictedCard = QtWidgets.QLabel(self.Player)
self.PredictedCard.setGeometry(QtCore.QRect(0, 0, 201, 61))
font = QtGui.QFont()
font.setFamily("等线")
font.setPointSize(14)
font.setBold(False)
font.setItalic(False)
font.setWeight(50)
self.PredictedCard.setFont(font)
self.PredictedCard.setStyleSheet("")
self.PredictedCard.setFrameShape(QtWidgets.QFrame.Panel)
self.PredictedCard.setLineWidth(1)
self.PredictedCard.setAlignment(QtCore.Qt.AlignCenter)
self.PredictedCard.setObjectName("PredictedCard")
self.ThreeLandlordCards = QtWidgets.QLabel(Form)
self.ThreeLandlordCards.setGeometry(QtCore.QRect(270, 20, 161, 41))
font = QtGui.QFont()
font.setFamily("等线")
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setWeight(50)
self.ThreeLandlordCards.setFont(font)
self.ThreeLandlordCards.setAlignment(QtCore.Qt.AlignCenter)
self.ThreeLandlordCards.setObjectName("ThreeLandlordCards")
self.Stop = QtWidgets.QPushButton(Form)
self.Stop.setGeometry(QtCore.QRect(230, 360, 111, 41))
font = QtGui.QFont()
font.setFamily("等线")
font.setPointSize(14)
font.setBold(False)
font.setItalic(False)
font.setWeight(50)
self.Stop.setFont(font)
self.Stop.setStyleSheet("")
self.Stop.setObjectName("Stop")
self.SwitchMode = QtWidgets.QPushButton(Form)
self.SwitchMode.setGeometry(QtCore.QRect(370, 360, 121, 41))
font = QtGui.QFont()
font.setFamily("等线")
font.setPointSize(14)
font.setBold(False)
font.setItalic(False)
font.setWeight(50)
self.SwitchMode.setFont(font)
self.SwitchMode.setStyleSheet("")
self.SwitchMode.setObjectName("SwitchMode")
self.AutoStart = QtWidgets.QPushButton(Form)
self.AutoStart.setGeometry(QtCore.QRect(520, 360, 111, 41))
font = QtGui.QFont()
font.setFamily("等线")
font.setPointSize(14)
font.setBold(False)
font.setItalic(False)
font.setWeight(50)
self.AutoStart.setFont(font)
self.AutoStart.setStyleSheet("")
self.AutoStart.setObjectName("AutoStart")
self.BidWinrate = QtWidgets.QLabel(Form)
self.BidWinrate.setGeometry(QtCore.QRect(50, 220, 241, 41))
font = QtGui.QFont()
font.setFamily("等线")
font.setPointSize(12)
font.setBold(False)
font.setItalic(False)
font.setWeight(50)
self.BidWinrate.setFont(font)
self.BidWinrate.setObjectName("BidWinrate")
self.PreWinrate = QtWidgets.QLabel(Form)
self.PreWinrate.setGeometry(QtCore.QRect(50, 270, 241, 41))
font = QtGui.QFont()
font.setFamily("等线")
font.setPointSize(12)
font.setBold(False)
font.setItalic(False)
font.setWeight(50)
self.PreWinrate.setFont(font)
self.PreWinrate.setObjectName("PreWinrate")
self.retranslateUi(Form)
self.InitCard.clicked.connect(Form.init_cards)
self.Stop.clicked.connect(Form.stop)
self.SwitchMode.clicked.connect(Form.switch_mode)
self.AutoStart.clicked.connect(Form.beforeStart)
QtCore.QMetaObject.connectSlotsByName(Form)
def retranslateUi(self, Form):
_translate = QtCore.QCoreApplication.translate
Form.setWindowTitle(_translate("Form", "Hi"))
self.WinRate.setText(_translate("Form", "评分"))
self.InitCard.setText(_translate("Form", "开始"))
self.UserHandCards.setText(_translate("Form", "手牌"))
self.LPlayedCard.setText(_translate("Form", "上家出牌区域"))
self.RPlayedCard.setText(_translate("Form", "下家出牌区域"))
self.PredictedCard.setText(_translate("Form", "AI出牌区域"))
self.ThreeLandlordCards.setText(_translate("Form", "地主牌"))
self.Stop.setText(_translate("Form", "停止"))
self.SwitchMode.setText(_translate("Form", "单局"))
self.AutoStart.setText(_translate("Form", "自动开始"))
self.BidWinrate.setText(_translate("Form", "叫牌预估胜率："))
self.PreWinrate.setText(_translate("Form", "局前预估胜率："))

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MainWindow.ui Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<ui version="4.0">
<class>Form</class>
<widget class="QWidget" name="Form">
<property name="geometry">
<rect>
<x>0</x>
<y>0</y>
<width>703</width>
<height>421</height>
</rect>
</property>
<property name="font">
<font>
<family>Arial</family>
<pointsize>9</pointsize>
<weight>75</weight>
<italic>false</italic>
<bold>true</bold>
</font>
</property>
<property name="windowTitle">
<string>Hi</string>
</property>
<property name="windowOpacity">
<double>0.800000000000000</double>
</property>
<widget class="QLabel" name="WinRate">
<property name="geometry">
<rect>
<x>480</x>
<y>150</y>
<width>201</width>
<height>61</height>
</rect>
</property>
<property name="font">
<font>
<family>等线</family>
<pointsize>14</pointsize>
<weight>50</weight>
<italic>false</italic>
<bold>false</bold>
</font>
</property>
<property name="text">
<string>评分</string>
</property>
<property name="alignment">
<set>Qt::AlignCenter</set>
</property>
</widget>
<widget class="QPushButton" name="InitCard">
<property name="geometry">
<rect>
<x>80</x>
<y>360</y>
<width>121</width>
<height>41</height>
</rect>
</property>
<property name="font">
<font>
<family>等线</family>
<pointsize>14</pointsize>
<weight>50</weight>
<italic>false</italic>
<bold>false</bold>
</font>
</property>
<property name="styleSheet">
<string notr="true"/>
</property>
<property name="text">
<string>开始</string>
</property>
</widget>
<widget class="QLabel" name="UserHandCards">
<property name="geometry">
<rect>
<x>40</x>
<y>160</y>
<width>421</width>
<height>41</height>
</rect>
</property>
<property name="font">
<font>
<family>等线</family>
<pointsize>14</pointsize>
<weight>50</weight>
<italic>false</italic>
<bold>false</bold>
</font>
</property>
<property name="text">
<string>手牌</string>
</property>
<property name="alignment">
<set>Qt::AlignCenter</set>
</property>
</widget>
<widget class="QFrame" name="LPlayer">
<property name="geometry">
<rect>
<x>20</x>
<y>80</y>
<width>201</width>
<height>61</height>
</rect>
</property>
<property name="font">
<font>
<family>等线</family>
<pointsize>9</pointsize>
<weight>50</weight>
<italic>false</italic>
<bold>false</bold>
</font>
</property>
<property name="frameShape">
<enum>QFrame::StyledPanel</enum>
</property>
<property name="frameShadow">
<enum>QFrame::Raised</enum>
</property>
<widget class="QLabel" name="LPlayedCard">
<property name="geometry">
<rect>
<x>0</x>
<y>0</y>
<width>201</width>
<height>61</height>
</rect>
</property>
<property name="font">
<font>
<family>等线</family>
<pointsize>14</pointsize>
<weight>50</weight>
<italic>false</italic>
<bold>false</bold>
</font>
</property>
<property name="text">
<string>上家出牌区域</string>
</property>
<property name="alignment">
<set>Qt::AlignCenter</set>
</property>
</widget>
</widget>
<widget class="QFrame" name="RPlayer">
<property name="geometry">
<rect>
<x>250</x>
<y>80</y>
<width>201</width>
<height>61</height>
</rect>
</property>
<property name="font">
<font>
<family>等线</family>
<pointsize>16</pointsize>
<weight>50</weight>
<italic>false</italic>
<bold>false</bold>
</font>
</property>
<property name="frameShape">
<enum>QFrame::StyledPanel</enum>
</property>
<property name="frameShadow">
<enum>QFrame::Raised</enum>
</property>
<widget class="QLabel" name="RPlayedCard">
<property name="geometry">
<rect>
<x>0</x>
<y>0</y>
<width>201</width>
<height>61</height>
</rect>
</property>
<property name="font">
<font>
<family>等线</family>
<pointsize>14</pointsize>
<weight>50</weight>
<italic>false</italic>
<bold>false</bold>
</font>
</property>
<property name="text">
<string>下家出牌区域</string>
</property>
<property name="alignment">
<set>Qt::AlignCenter</set>
</property>
</widget>
</widget>
<widget class="QFrame" name="Player">
<property name="geometry">
<rect>
<x>480</x>
<y>80</y>
<width>201</width>
<height>61</height>
</rect>
</property>
<property name="font">
<font>
<family>等线</family>
<pointsize>9</pointsize>
<weight>50</weight>
<italic>false</italic>
<bold>false</bold>
</font>
</property>
<property name="frameShape">
<enum>QFrame::StyledPanel</enum>
</property>
<property name="frameShadow">
<enum>QFrame::Raised</enum>
</property>
<widget class="QLabel" name="PredictedCard">
<property name="geometry">
<rect>
<x>0</x>
<y>0</y>
<width>201</width>
<height>61</height>
</rect>
</property>
<property name="font">
<font>
<family>等线</family>
<pointsize>14</pointsize>
<weight>50</weight>
<italic>false</italic>
<bold>false</bold>
</font>
</property>
<property name="styleSheet">
<string notr="true"/>
</property>
<property name="frameShape">
<enum>QFrame::Panel</enum>
</property>
<property name="lineWidth">
<number>1</number>
</property>
<property name="text">
<string>AI出牌区域</string>
</property>
<property name="alignment">
<set>Qt::AlignCenter</set>
</property>
</widget>
</widget>
<widget class="QLabel" name="ThreeLandlordCards">
<property name="geometry">
<rect>
<x>270</x>
<y>20</y>
<width>161</width>
<height>41</height>
</rect>
</property>
<property name="font">
<font>
<family>等线</family>
<pointsize>16</pointsize>
<weight>50</weight>
<italic>false</italic>
<bold>false</bold>
</font>
</property>
<property name="text">
<string>地主牌</string>
</property>
<property name="alignment">
<set>Qt::AlignCenter</set>
</property>
</widget>
<widget class="QPushButton" name="Stop">
<property name="geometry">
<rect>
<x>230</x>
<y>360</y>
<width>111</width>
<height>41</height>
</rect>
</property>
<property name="font">
<font>
<family>等线</family>
<pointsize>14</pointsize>
<weight>50</weight>
<italic>false</italic>
<bold>false</bold>
</font>
</property>
<property name="styleSheet">
<string notr="true"/>
</property>
<property name="text">
<string>停止</string>
</property>
</widget>
<widget class="QPushButton" name="SwitchMode">
<property name="geometry">
<rect>
<x>370</x>
<y>360</y>
<width>121</width>
<height>41</height>
</rect>
</property>
<property name="font">
<font>
<family>等线</family>
<pointsize>14</pointsize>
<weight>50</weight>
<italic>false</italic>
<bold>false</bold>
</font>
</property>
<property name="styleSheet">
<string notr="true"/>
</property>
<property name="text">
<string>单局</string>
</property>
</widget>
<widget class="QPushButton" name="AutoStart">
<property name="geometry">
<rect>
<x>520</x>
<y>360</y>
<width>111</width>
<height>41</height>
</rect>
</property>
<property name="font">
<font>
<family>等线</family>
<pointsize>14</pointsize>
<weight>50</weight>
<italic>false</italic>
<bold>false</bold>
</font>
</property>
<property name="styleSheet">
<string notr="true"/>
</property>
<property name="text">
<string>自动开始</string>
</property>
</widget>
<widget class="QLabel" name="BidWinrate">
<property name="geometry">
<rect>
<x>50</x>
<y>220</y>
<width>241</width>
<height>41</height>
</rect>
</property>
<property name="font">
<font>
<family>等线</family>
<pointsize>12</pointsize>
<weight>50</weight>
<italic>false</italic>
<bold>false</bold>
</font>
</property>
<property name="text">
<string>叫牌预估胜率：</string>
</property>
</widget>
<widget class="QLabel" name="PreWinrate">
<property name="geometry">
<rect>
<x>50</x>
<y>270</y>
<width>241</width>
<height>41</height>
</rect>
</property>
<property name="font">
<font>
<family>等线</family>
<pointsize>12</pointsize>
<weight>50</weight>
<italic>false</italic>
<bold>false</bold>
</font>
</property>
<property name="text">
<string>局前预估胜率：</string>
</property>
</widget>
</widget>
<resources/>
<connections>
<connection>
<sender>InitCard</sender>
<signal>clicked()</signal>
<receiver>Form</receiver>
<slot>init_cards()</slot>
<hints>
<hint type="sourcelabel">
<x>200</x>
<y>360</y>
</hint>
<hint type="destinationlabel">
<x>250</x>
<y>292</y>
</hint>
</hints>
</connection>
<connection>
<sender>Stop</sender>
<signal>clicked()</signal>
<receiver>Form</receiver>
<slot>stop()</slot>
<hints>
<hint type="sourcelabel">
<x>230</x>
<y>360</y>
</hint>
<hint type="destinationlabel">
<x>233</x>
<y>220</y>
</hint>
</hints>
</connection>
<connection>
<sender>SwitchMode</sender>
<signal>clicked()</signal>
<receiver>Form</receiver>
<slot>switch_mode()</slot>
<hints>
<hint type="sourcelabel">
<x>472</x>
<y>366</y>
</hint>
<hint type="destinationlabel">
<x>480</x>
<y>277</y>
</hint>
</hints>
</connection>
<connection>
<sender>AutoStart</sender>
<signal>clicked()</signal>
<receiver>Form</receiver>
<slot>beforeStart()</slot>
<hints>
<hint type="sourcelabel">
<x>613</x>
<y>372</y>
</hint>
<hint type="destinationlabel">
<x>646</x>
<y>291</y>
</hint>
</hints>
</connection>
</connections>
<slots>
<slot>init_cards()</slot>
<slot>start()</slot>
<slot>stop()</slot>
<slot>switch_mode()</slot>
<slot>beforeStart()</slot>
</slots>
</ui>

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MainWindowUI.py Normal file
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# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'MainWindow.ui'
#
# Created by: PyQt5 UI code generator 5.13.0
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Form(object):
def setupUi(self, Form):
Form.setObjectName("Form")
Form.resize(440, 450)
font = QtGui.QFont()
font.setFamily("Arial")
font.setPointSize(9)
font.setBold(True)
font.setItalic(False)
font.setWeight(75)
Form.setFont(font)
self.WinRate = QtWidgets.QLabel(Form)
self.WinRate.setGeometry(QtCore.QRect(240, 180, 171, 61))
font = QtGui.QFont()
font.setPointSize(14)
self.WinRate.setFont(font)
self.WinRate.setAlignment(QtCore.Qt.AlignCenter)
self.WinRate.setObjectName("WinRate")
self.InitCard = QtWidgets.QPushButton(Form)
self.InitCard.setGeometry(QtCore.QRect(60, 330, 121, 41))
font = QtGui.QFont()
font.setFamily("Arial")
font.setPointSize(14)
font.setBold(True)
font.setWeight(75)
self.InitCard.setFont(font)
self.InitCard.setStyleSheet("")
self.InitCard.setObjectName("InitCard")
self.SwitchMode = QtWidgets.QPushButton(Form)
self.SwitchMode.setGeometry(QtCore.QRect(60, 380, 121, 41))
font = QtGui.QFont()
font.setFamily("Arial")
font.setPointSize(14)
font.setBold(True)
font.setWeight(75)
self.SwitchMode.setFont(font)
self.SwitchMode.setStyleSheet("")
self.SwitchMode.setObjectName("SwitchMode")
self.AutoStart = QtWidgets.QPushButton(Form)
self.AutoStart.setGeometry(QtCore.QRect(260, 380, 111, 41))
font = QtGui.QFont()
font.setFamily("Arial")
font.setPointSize(14)
font.setBold(True)
font.setWeight(75)
self.AutoStart.setFont(font)
self.AutoStart.setStyleSheet("")
self.AutoStart.setObjectName("AutoStart")
self.UserHandCards = QtWidgets.QLabel(Form)
self.UserHandCards.setGeometry(QtCore.QRect(10, 260, 421, 41))
font = QtGui.QFont()
font.setPointSize(14)
self.UserHandCards.setFont(font)
self.UserHandCards.setAlignment(QtCore.Qt.AlignCenter)
self.UserHandCards.setObjectName("UserHandCards")
self.LPlayer = QtWidgets.QFrame(Form)
self.LPlayer.setGeometry(QtCore.QRect(10, 80, 201, 61))
self.LPlayer.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.LPlayer.setFrameShadow(QtWidgets.QFrame.Raised)
self.LPlayer.setObjectName("LPlayer")
self.LPlayedCard = QtWidgets.QLabel(self.LPlayer)
self.LPlayedCard.setGeometry(QtCore.QRect(0, 0, 201, 61))
font = QtGui.QFont()
font.setPointSize(14)
self.LPlayedCard.setFont(font)
self.LPlayedCard.setAlignment(QtCore.Qt.AlignCenter)
self.LPlayedCard.setObjectName("LPlayedCard")
self.RPlayer = QtWidgets.QFrame(Form)
self.RPlayer.setGeometry(QtCore.QRect(230, 80, 201, 61))
font = QtGui.QFont()
font.setPointSize(16)
self.RPlayer.setFont(font)
self.RPlayer.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.RPlayer.setFrameShadow(QtWidgets.QFrame.Raised)
self.RPlayer.setObjectName("RPlayer")
self.RPlayedCard = QtWidgets.QLabel(self.RPlayer)
self.RPlayedCard.setGeometry(QtCore.QRect(0, 0, 201, 61))
font = QtGui.QFont()
font.setPointSize(14)
self.RPlayedCard.setFont(font)
self.RPlayedCard.setAlignment(QtCore.Qt.AlignCenter)
self.RPlayedCard.setObjectName("RPlayedCard")
self.Player = QtWidgets.QFrame(Form)
self.Player.setGeometry(QtCore.QRect(40, 180, 171, 61))
self.Player.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.Player.setFrameShadow(QtWidgets.QFrame.Raised)
self.Player.setObjectName("Player")
self.PredictedCard = QtWidgets.QLabel(self.Player)
self.PredictedCard.setGeometry(QtCore.QRect(0, 0, 171, 61))
font = QtGui.QFont()
font.setPointSize(14)
self.PredictedCard.setFont(font)
self.PredictedCard.setAlignment(QtCore.Qt.AlignCenter)
self.PredictedCard.setObjectName("PredictedCard")
self.ThreeLandlordCards = QtWidgets.QLabel(Form)
self.ThreeLandlordCards.setGeometry(QtCore.QRect(140, 10, 161, 41))
font = QtGui.QFont()
font.setPointSize(16)
self.ThreeLandlordCards.setFont(font)
self.ThreeLandlordCards.setAlignment(QtCore.Qt.AlignCenter)
self.ThreeLandlordCards.setObjectName("ThreeLandlordCards")
self.Stop = QtWidgets.QPushButton(Form)
self.Stop.setGeometry(QtCore.QRect(260, 330, 111, 41))
font = QtGui.QFont()
font.setFamily("Arial")
font.setPointSize(14)
font.setBold(True)
font.setWeight(75)
self.Stop.setFont(font)
self.Stop.setStyleSheet("")
self.Stop.setObjectName("Stop")
self.retranslateUi(Form)
self.InitCard.clicked.connect(Form.init_cards)
self.Stop.clicked.connect(Form.stop)
self.SwitchMode.clicked.connect(Form.switch_mode)
self.AutoStart.clicked.connect(Form.beforeStart)
QtCore.QMetaObject.connectSlotsByName(Form)
def retranslateUi(self, Form):
_translate = QtCore.QCoreApplication.translate
Form.setWindowTitle(_translate("Form", "Hi"))
self.WinRate.setText(_translate("Form", ""))
self.InitCard.setText(_translate("Form", "开始"))
self.SwitchMode.setText(_translate("Form", "单局"))
self.AutoStart.setText(_translate("Form", "自动开始"))
self.UserHandCards.setText(_translate("Form", "手牌"))
self.LPlayedCard.setText(_translate("Form", "上家出牌区域"))
self.RPlayedCard.setText(_translate("Form", "下家出牌区域"))
self.PredictedCard.setText(_translate("Form", "AI出牌区域"))
self.ThreeLandlordCards.setText(_translate("Form", "三张底牌"))
self.Stop.setText(_translate("Form", "停止"))

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# DouZero_For_HLDDZ_FullAuto: 将DouZero用于欢乐斗地主自动化
* 本项目基于[DouZero](https://github.com/kwai/DouZero) 和 [DouZero_For_Happy_DouDiZhu](https://github.com/tianqiraf/DouZero_For_HappyDouDiZhu)
* 环境配置请移步项目DouZero
* 模型默认为ADP，更换模型请修改main.py中的模型路径
* 运行main.py即可
* 在原 [DouZero_For_Happy_DouDiZhu](https://github.com/tianqiraf/DouZero_For_HappyDouDiZhu) 的基础上加入了自动出牌，基于手牌自动叫牌，加倍，同时修改截屏方式为窗口区域截屏，游戏原窗口遮挡不影响游戏进行。
* **请勿把游戏界面最小化，否则无法使用**
## 说明
* 欢乐斗地主使用窗口模式运行
* **本项目仅供学习以及技术交流，请勿用于其它目的，否则后果自负。**
## 使用步骤
1. 点击游戏中开始游戏后点击程序的`自动开始`
## 潜在Bug
* 有较低几率把出牌识别为不出，从而卡在自己出牌阶段。
## 鸣谢
* 本项目基于[DouZero](https://github.com/kwai/DouZero) [DouZero_For_Happy_DouDiZhu](https://github.com/tianqiraf/DouZero_For_HappyDouDiZhu)

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from .dmc import train
from .arguments import parser

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import argparse
parser = argparse.ArgumentParser(description='DouZero: PyTorch DouDizhu AI')
# General Settings
parser.add_argument('--xpid', default='douzero',
help='Experiment id (default: douzero)')
parser.add_argument('--save_interval', default=30, type=int,
help='Time interval (in minutes) at which to save the model')
parser.add_argument('--objective', default='adp', type=str, choices=['adp', 'wp'],
help='Use ADP or WP as reward (default: ADP)')
# Training settings
parser.add_argument('--gpu_devices', default='0', type=str,
help='Which GPUs to be used for training')
parser.add_argument('--num_actor_devices', default=1, type=int,
help='The number of devices used for simulation')
parser.add_argument('--num_actors', default=5, type=int,
help='The number of actors for each simulation device')
parser.add_argument('--training_device', default=0, type=int,
help='The index of the GPU used for training models')
parser.add_argument('--load_model', action='store_true',
help='Load an existing model')
parser.add_argument('--disable_checkpoint', action='store_true',
help='Disable saving checkpoint')
parser.add_argument('--savedir', default='douzero_checkpoints',
help='Root dir where experiment data will be saved')
# Hyperparameters
parser.add_argument('--total_frames', default=100000000000, type=int,
help='Total environment frames to train for')
parser.add_argument('--exp_epsilon', default=0.01, type=float,
help='The probability for exploration')
parser.add_argument('--batch_size', default=32, type=int,
help='Learner batch size')
parser.add_argument('--unroll_length', default=100, type=int,
help='The unroll length (time dimension)')
parser.add_argument('--num_buffers', default=50, type=int,
help='Number of shared-memory buffers')
parser.add_argument('--num_threads', default=4, type=int,
help='Number learner threads')
parser.add_argument('--max_grad_norm', default=40., type=float,
help='Max norm of gradients')
# Optimizer settings
parser.add_argument('--learning_rate', default=0.0001, type=float,
help='Learning rate')
parser.add_argument('--alpha', default=0.99, type=float,
help='RMSProp smoothing constant')
parser.add_argument('--momentum', default=0, type=float,
help='RMSProp momentum')
parser.add_argument('--epsilon', default=1e-5, type=float,
help='RMSProp epsilon')

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douzero/dmc/dmc.py Normal file
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import os
import threading
import time
import timeit
import pprint
from collections import deque
import torch
from torch import multiprocessing as mp
from torch import nn
from .file_writer import FileWriter
from .models import Model
from .utils import get_batch, log, create_env, create_buffers, create_optimizers, act
mean_episode_return_buf = {p:deque(maxlen=100) for p in ['landlord', 'landlord_up', 'landlord_down']}
def compute_loss(logits, targets):
loss = ((logits.squeeze(-1) - targets)**2).mean()
return loss
def learn(position,
actor_models,
model,
batch,
optimizer,
flags,
lock):
"""Performs a learning (optimization) step."""
device = torch.device('cuda:'+str(flags.training_device))
obs_x_no_action = batch['obs_x_no_action'].to(device)
obs_action = batch['obs_action'].to(device)
obs_x = torch.cat((obs_x_no_action, obs_action), dim=2).float()
obs_x = torch.flatten(obs_x, 0, 1)
obs_z = torch.flatten(batch['obs_z'].to(device), 0, 1).float()
target = torch.flatten(batch['target'].to(device), 0, 1)
episode_returns = batch['episode_return'][batch['done']]
mean_episode_return_buf[position].append(torch.mean(episode_returns).to(device))
with lock:
learner_outputs = model(obs_z, obs_x, return_value=True)
loss = compute_loss(learner_outputs['values'], target)
stats = {
'mean_episode_return_'+position: torch.mean(torch.stack([_r for _r in mean_episode_return_buf[position]])).item(),
'loss_'+position: loss.item(),
}
optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), flags.max_grad_norm)
optimizer.step()
for actor_model in actor_models:
actor_model.get_model(position).load_state_dict(model.state_dict())
return stats
def train(flags):
"""
This is the main funtion for training. It will first
initilize everything, such as buffers, optimizers, etc.
Then it will start subprocesses as actors. Then, it will call
learning function with multiple threads.
"""
plogger = FileWriter(
xpid=flags.xpid,
xp_args=flags.__dict__,
rootdir=flags.savedir,
)
checkpointpath = os.path.expandvars(
os.path.expanduser('%s/%s/%s' % (flags.savedir, flags.xpid, 'model.tar')))
T = flags.unroll_length
B = flags.batch_size
# Initialize actor models
models = []
assert flags.num_actor_devices <= len(flags.gpu_devices.split(',')), 'The number of actor devices can not exceed the number of available devices'
for device in range(flags.num_actor_devices):
model = Model(device=device)
model.share_memory()
model.eval()
models.append(model)
# Initialize buffers
buffers = create_buffers(flags)
# Initialize queues
actor_processes = []
ctx = mp.get_context('spawn')
free_queue = []
full_queue = []
for device in range(flags.num_actor_devices):
_free_queue = {'landlord': ctx.SimpleQueue(), 'landlord_up': ctx.SimpleQueue(), 'landlord_down': ctx.SimpleQueue()}
_full_queue = {'landlord': ctx.SimpleQueue(), 'landlord_up': ctx.SimpleQueue(), 'landlord_down': ctx.SimpleQueue()}
free_queue.append(_free_queue)
full_queue.append(_full_queue)
# Learner model for training
learner_model = Model(device=flags.training_device)
# Create optimizers
optimizers = create_optimizers(flags, learner_model)
# Stat Keys
stat_keys = [
'mean_episode_return_landlord',
'loss_landlord',
'mean_episode_return_landlord_up',
'loss_landlord_up',
'mean_episode_return_landlord_down',
'loss_landlord_down',
]
frames, stats = 0, {k: 0 for k in stat_keys}
position_frames = {'landlord':0, 'landlord_up':0, 'landlord_down':0}
# Load models if any
if flags.load_model and os.path.exists(checkpointpath):
checkpoint_states = torch.load(
checkpointpath, map_location="cuda:"+str(flags.training_device)
)
for k in ['landlord', 'landlord_up', 'landlord_down']:
learner_model.get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k])
optimizers[k].load_state_dict(checkpoint_states["optimizer_state_dict"][k])
for device in range(flags.num_actor_devices):
models[device].get_model(k).load_state_dict(learner_model.get_model(k).state_dict())
stats = checkpoint_states["stats"]
frames = checkpoint_states["frames"]
position_frames = checkpoint_states["position_frames"]
log.info(f"Resuming preempted job, current stats:\n{stats}")
# Starting actor processes
for device in range(flags.num_actor_devices):
num_actors = flags.num_actors
for i in range(flags.num_actors):
actor = ctx.Process(
target=act,
args=(i, device, free_queue[device], full_queue[device], models[device], buffers[device], flags))
actor.start()
actor_processes.append(actor)
def batch_and_learn(i, device, position, local_lock, position_lock, lock=threading.Lock()):
"""Thread target for the learning process."""
nonlocal frames, position_frames, stats
while frames < flags.total_frames:
batch = get_batch(free_queue[device][position], full_queue[device][position], buffers[device][position], flags, local_lock)
_stats = learn(position, models, learner_model.get_model(position), batch,
optimizers[position], flags, position_lock)
with lock:
for k in _stats:
stats[k] = _stats[k]
to_log = dict(frames=frames)
to_log.update({k: stats[k] for k in stat_keys})
plogger.log(to_log)
frames += T * B
position_frames[position] += T * B
for device in range(flags.num_actor_devices):
for m in range(flags.num_buffers):
free_queue[device]['landlord'].put(m)
free_queue[device]['landlord_up'].put(m)
free_queue[device]['landlord_down'].put(m)
threads = []
locks = [{'landlord': threading.Lock(), 'landlord_up': threading.Lock(), 'landlord_down': threading.Lock()} for _ in range(flags.num_actor_devices)]
position_locks = {'landlord': threading.Lock(), 'landlord_up': threading.Lock(), 'landlord_down': threading.Lock()}
for device in range(flags.num_actor_devices):
for i in range(flags.num_threads):
for position in ['landlord', 'landlord_up', 'landlord_down']:
thread = threading.Thread(
target=batch_and_learn, name='batch-and-learn-%d' % i, args=(i,device,position,locks[device][position],position_locks[position]))
thread.start()
threads.append(thread)
def checkpoint(frames):
if flags.disable_checkpoint:
return
log.info('Saving checkpoint to %s', checkpointpath)
_models = learner_model.get_models()
torch.save({
'model_state_dict': {k: _models[k].state_dict() for k in _models},
'optimizer_state_dict': {k: optimizers[k].state_dict() for k in optimizers},
"stats": stats,
'flags': vars(flags),
'frames': frames,
'position_frames': position_frames
}, checkpointpath)
# Save the weights for evaluation purpose
for position in ['landlord', 'landlord_up', 'landlord_down']:
model_weights_dir = os.path.expandvars(os.path.expanduser(
'%s/%s/%s' % (flags.savedir, flags.xpid, position+'_weights_'+str(frames)+'.ckpt')))
torch.save(learner_model.get_model(position).state_dict(), model_weights_dir)
timer = timeit.default_timer
try:
last_checkpoint_time = timer() - flags.save_interval * 60
while frames < flags.total_frames:
start_frames = frames
position_start_frames = {k: position_frames[k] for k in position_frames}
start_time = timer()
time.sleep(5)
if timer() - last_checkpoint_time > flags.save_interval * 60:
checkpoint(frames)
last_checkpoint_time = timer()
end_time = timer()
fps = (frames - start_frames) / (end_time - start_time)
position_fps = {k:(position_frames[k]-position_start_frames[k])/(end_time-start_time) for k in position_frames}
log.info('After %i (L:%i U:%i D:%i) frames: @ %.1f fps (L:%.1f U:%.1f D:%.1f) Stats:\n%s',
frames,
position_frames['landlord'],
position_frames['landlord_up'],
position_frames['landlord_down'],
fps,
position_fps['landlord'],
position_fps['landlord_up'],
position_fps['landlord_down'],
pprint.pformat(stats))
except KeyboardInterrupt:
return
else:
for thread in threads:
thread.join()
log.info('Learning finished after %d frames.', frames)
checkpoint(frames)
plogger.close()

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"""
Here, we wrap the original environment to make it easier
to use. When a game is finished, instead of mannualy reseting
the environment, we do it automatically.
"""
import numpy as np
import torch
def _format_observation(obs, device):
"""
A utility function to process observations and
move them to CUDA.
"""
position = obs['position']
device = torch.device('cuda:'+str(device))
x_batch = torch.from_numpy(obs['x_batch']).to(device)
z_batch = torch.from_numpy(obs['z_batch']).to(device)
x_no_action = torch.from_numpy(obs['x_no_action'])
z = torch.from_numpy(obs['z'])
obs = {'x_batch': x_batch,
'z_batch': z_batch,
'legal_actions': obs['legal_actions'],
}
return position, obs, x_no_action, z
class Environment:
def __init__(self, env, device):
""" Initialzie this environment wrapper
"""
self.env = env
self.device = device
self.episode_return = None
def initial(self):
initial_position, initial_obs, x_no_action, z = _format_observation(self.env.reset(), self.device)
initial_reward = torch.zeros(1, 1)
self.episode_return = torch.zeros(1, 1)
initial_done = torch.ones(1, 1, dtype=torch.bool)
return initial_position, initial_obs, dict(
done=initial_done,
episode_return=self.episode_return,
obs_x_no_action=x_no_action,
obs_z=z,
)
def step(self, action):
obs, reward, done, _ = self.env.step(action)
self.episode_return += reward
episode_return = self.episode_return
if done:
obs = self.env.reset()
self.episode_return = torch.zeros(1, 1)
position, obs, x_no_action, z = _format_observation(obs, self.device)
reward = torch.tensor(reward).view(1, 1)
done = torch.tensor(done).view(1, 1)
return position, obs, dict(
done=done,
episode_return=episode_return,
obs_x_no_action=x_no_action,
obs_z=z,
)
def close(self):
self.env.close()

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# Copyright (c) Facebook, Inc. and its affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import datetime
import csv
import json
import logging
import os
import time
from typing import Dict
# import git
# def gather_metadata() -> Dict:
# date_start = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')
# # gathering git metadata
# try:
# repo = git.Repo(search_parent_directories=True)
# git_sha = repo.commit().hexsha
# git_data = dict(
# commit=git_sha,
# branch=repo.active_branch.name,
# is_dirty=repo.is_dirty(),
# path=repo.git_dir,
# )
# except git.InvalidGitRepositoryError:
# git_data = None
# # gathering slurm metadata
# if 'SLURM_JOB_ID' in os.environ:
# slurm_env_keys = [k for k in os.environ if k.startswith('SLURM')]
# slurm_data = {}
# for k in slurm_env_keys:
# d_key = k.replace('SLURM_', '').replace('SLURMD_', '').lower()
# slurm_data[d_key] = os.environ[k]
# else:
# slurm_data = None
# return dict(
# date_start=date_start,
# date_end=None,
# successful=False,
# git=git_data,
# slurm=slurm_data,
# env=os.environ.copy(),
# )
class FileWriter:
def __init__(self,
xpid: str = None,
xp_args: dict = None,
rootdir: str = '~/palaas'):
if not xpid:
# make unique id
xpid = '{proc}_{unixtime}'.format(
proc=os.getpid(), unixtime=int(time.time()))
self.xpid = xpid
self._tick = 0
# metadata gathering
if xp_args is None:
xp_args = {}
self.metadata = gather_metadata()
# we need to copy the args, otherwise when we close the file writer
# (and rewrite the args) we might have non-serializable objects (or
# other nasty stuff).
self.metadata['args'] = copy.deepcopy(xp_args)
self.metadata['xpid'] = self.xpid
formatter = logging.Formatter('%(message)s')
self._logger = logging.getLogger('palaas/out')
# to stdout handler
shandle = logging.StreamHandler()
shandle.setFormatter(formatter)
self._logger.addHandler(shandle)
self._logger.setLevel(logging.INFO)
rootdir = os.path.expandvars(os.path.expanduser(rootdir))
# to file handler
self.basepath = os.path.join(rootdir, self.xpid)
if not os.path.exists(self.basepath):
self._logger.info('Creating log directory: %s', self.basepath)
os.makedirs(self.basepath, exist_ok=True)
else:
self._logger.info('Found log directory: %s', self.basepath)
# NOTE: remove latest because it creates errors when running on slurm
# multiple jobs trying to write to latest but cannot find it
# Add 'latest' as symlink unless it exists and is no symlink.
# symlink = os.path.join(rootdir, 'latest')
# if os.path.islink(symlink):
# os.remove(symlink)
# if not os.path.exists(symlink):
# os.symlink(self.basepath, symlink)
# self._logger.info('Symlinked log directory: %s', symlink)
self.paths = dict(
msg='{base}/out.log'.format(base=self.basepath),
logs='{base}/logs.csv'.format(base=self.basepath),
fields='{base}/fields.csv'.format(base=self.basepath),
meta='{base}/meta.json'.format(base=self.basepath),
)
self._logger.info('Saving arguments to %s', self.paths['meta'])
if os.path.exists(self.paths['meta']):
self._logger.warning('Path to meta file already exists. '
'Not overriding meta.')
else:
self._save_metadata()
self._logger.info('Saving messages to %s', self.paths['msg'])
if os.path.exists(self.paths['msg']):
self._logger.warning('Path to message file already exists. '
'New data will be appended.')
fhandle = logging.FileHandler(self.paths['msg'])
fhandle.setFormatter(formatter)
self._logger.addHandler(fhandle)
self._logger.info('Saving logs data to %s', self.paths['logs'])
self._logger.info('Saving logs\' fields to %s', self.paths['fields'])
if os.path.exists(self.paths['logs']):
self._logger.warning('Path to log file already exists. '
'New data will be appended.')
with open(self.paths['fields'], 'r') as csvfile:
reader = csv.reader(csvfile)
self.fieldnames = list(reader)[0]
else:
self.fieldnames = ['_tick', '_time']
def log(self, to_log: Dict, tick: int = None,
verbose: bool = False) -> None:
if tick is not None:
raise NotImplementedError
else:
to_log['_tick'] = self._tick
self._tick += 1
to_log['_time'] = time.time()
old_len = len(self.fieldnames)
for k in to_log:
if k not in self.fieldnames:
self.fieldnames.append(k)
if old_len != len(self.fieldnames):
with open(self.paths['fields'], 'w') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(self.fieldnames)
self._logger.info('Updated log fields: %s', self.fieldnames)
if to_log['_tick'] == 0:
# print("\ncreating logs file ")
with open(self.paths['logs'], 'a') as f:
f.write('# %s\n' % ','.join(self.fieldnames))
if verbose:
self._logger.info('LOG | %s', ', '.join(
['{}: {}'.format(k, to_log[k]) for k in sorted(to_log)]))
with open(self.paths['logs'], 'a') as f:
writer = csv.DictWriter(f, fieldnames=self.fieldnames)
writer.writerow(to_log)
# print("\nadded to log file")
def close(self, successful: bool = True) -> None:
self.metadata['date_end'] = datetime.datetime.now().strftime(
'%Y-%m-%d %H:%M:%S.%f')
self.metadata['successful'] = successful
self._save_metadata()
def _save_metadata(self) -> None:
with open(self.paths['meta'], 'w') as jsonfile:
json.dump(self.metadata, jsonfile, indent=4, sort_keys=True)

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"""
This file includes the torch models. We wrap the three
models into one class for convenience.
"""
import numpy as np
import torch
from torch import nn
class LandlordLstmModel(nn.Module):
def __init__(self):
super().__init__()
self.lstm = nn.LSTM(162, 128, batch_first=True)
self.dense1 = nn.Linear(373 + 128, 512)
self.dense2 = nn.Linear(512, 512)
self.dense3 = nn.Linear(512, 512)
self.dense4 = nn.Linear(512, 512)
self.dense5 = nn.Linear(512, 512)
self.dense6 = nn.Linear(512, 1)
def forward(self, z, x, return_value=False, flags=None):
lstm_out, (h_n, _) = self.lstm(z)
lstm_out = lstm_out[:,-1,:]
x = torch.cat([lstm_out,x], dim=-1)
x = self.dense1(x)
x = torch.relu(x)
x = self.dense2(x)
x = torch.relu(x)
x = self.dense3(x)
x = torch.relu(x)
x = self.dense4(x)
x = torch.relu(x)
x = self.dense5(x)
x = torch.relu(x)
x = self.dense6(x)
if return_value:
return dict(values=x)
else:
if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon:
action = torch.randint(x.shape[0], (1,))[0]
else:
action = torch.argmax(x,dim=0)[0]
return dict(action=action)
class FarmerLstmModel(nn.Module):
def __init__(self):
super().__init__()
self.lstm = nn.LSTM(162, 128, batch_first=True)
self.dense1 = nn.Linear(484 + 128, 512)
self.dense2 = nn.Linear(512, 512)
self.dense3 = nn.Linear(512, 512)
self.dense4 = nn.Linear(512, 512)
self.dense5 = nn.Linear(512, 512)
self.dense6 = nn.Linear(512, 1)
def forward(self, z, x, return_value=False, flags=None):
lstm_out, (h_n, _) = self.lstm(z)
lstm_out = lstm_out[:,-1,:]
x = torch.cat([lstm_out,x], dim=-1)
x = self.dense1(x)
x = torch.relu(x)
x = self.dense2(x)
x = torch.relu(x)
x = self.dense3(x)
x = torch.relu(x)
x = self.dense4(x)
x = torch.relu(x)
x = self.dense5(x)
x = torch.relu(x)
x = self.dense6(x)
if return_value:
return dict(values=x)
else:
if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon:
action = torch.randint(x.shape[0], (1,))[0]
else:
action = torch.argmax(x,dim=0)[0]
return dict(action=action)
# Model dict is only used in evaluation but not training
model_dict = {}
model_dict['landlord'] = LandlordLstmModel
model_dict['landlord_up'] = FarmerLstmModel
model_dict['landlord_down'] = FarmerLstmModel
class Model:
"""
The wrapper for the three models. We also wrap several
interfaces such as share_memory, eval, etc.
"""
def __init__(self, device=0):
self.models = {}
self.models['landlord'] = LandlordLstmModel().to(torch.device('cuda:'+str(device)))
self.models['landlord_up'] = FarmerLstmModel().to(torch.device('cuda:'+str(device)))
self.models['landlord_down'] = FarmerLstmModel().to(torch.device('cuda:'+str(device)))
def forward(self, position, z, x, training=False, flags=None):
model = self.models[position]
return model.forward(z, x, training, flags)
def share_memory(self):
self.models['landlord'].share_memory()
self.models['landlord_up'].share_memory()
self.models['landlord_down'].share_memory()
def eval(self):
self.models['landlord'].eval()
self.models['landlord_up'].eval()
self.models['landlord_down'].eval()
def parameters(self, position):
return self.models[position].parameters()
def get_model(self, position):
return self.models[position]
def get_models(self):
return self.models

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import os
import typing
import logging
import traceback
import numpy as np
from collections import Counter
import time
import torch
from torch import multiprocessing as mp
from .env_utils import Environment
from douzero.env import Env
Card2Column = {3: 0, 4: 1, 5: 2, 6: 3, 7: 4, 8: 5, 9: 6, 10: 7,
11: 8, 12: 9, 13: 10, 14: 11, 17: 12}
NumOnes2Array = {0: np.array([0, 0, 0, 0]),
1: np.array([1, 0, 0, 0]),
2: np.array([1, 1, 0, 0]),
3: np.array([1, 1, 1, 0]),
4: np.array([1, 1, 1, 1])}
shandle = logging.StreamHandler()
shandle.setFormatter(
logging.Formatter(
'[%(levelname)s:%(process)d %(module)s:%(lineno)d %(asctime)s] '
'%(message)s'))
log = logging.getLogger('doudzero')
log.propagate = False
log.addHandler(shandle)
log.setLevel(logging.INFO)
# Buffers are used to transfer data between actor processes
# and learner processes. They are shared tensors in GPU
Buffers = typing.Dict[str, typing.List[torch.Tensor]]
def create_env(flags):
return Env(flags.objective)
def get_batch(free_queue,
full_queue,
buffers,
flags,
lock):
"""
This function will sample a batch from the buffers based
on the indices received from the full queue. It will also
free the indices by sending it to full_queue.
"""
with lock:
indices = [full_queue.get() for _ in range(flags.batch_size)]
batch = {
key: torch.stack([buffers[key][m] for m in indices], dim=1)
for key in buffers
}
for m in indices:
free_queue.put(m)
return batch
def create_optimizers(flags, learner_model):
"""
Create three optimizers for the three positions
"""
positions = ['landlord', 'landlord_up', 'landlord_down']
optimizers = {}
for position in positions:
optimizer = torch.optim.RMSprop(
learner_model.parameters(position),
lr=flags.learning_rate,
momentum=flags.momentum,
eps=flags.epsilon,
alpha=flags.alpha)
optimizers[position] = optimizer
return optimizers
def create_buffers(flags):
"""
We create buffers for different positions as well as
for different devices (i.e., GPU). That is, each device
will have three buffers for the three positions.
"""
T = flags.unroll_length
positions = ['landlord', 'landlord_up', 'landlord_down']
buffers = []
for device in range(torch.cuda.device_count()):
buffers.append({})
for position in positions:
x_dim = 319 if position == 'landlord' else 430
specs = dict(
done=dict(size=(T,), dtype=torch.bool),
episode_return=dict(size=(T,), dtype=torch.float32),
target=dict(size=(T,), dtype=torch.float32),
obs_x_no_action=dict(size=(T, x_dim), dtype=torch.int8),
obs_action=dict(size=(T, 54), dtype=torch.int8),
obs_z=dict(size=(T, 5, 162), dtype=torch.int8),
)
_buffers: Buffers = {key: [] for key in specs}
for _ in range(flags.num_buffers):
for key in _buffers:
_buffer = torch.empty(**specs[key]).to(torch.device('cuda:'+str(device))).share_memory_()
_buffers[key].append(_buffer)
buffers[device][position] = _buffers
return buffers
def act(i, device, free_queue, full_queue, model, buffers, flags):
"""
This function will run forever until we stop it. It will generate
data from the environment and send the data to buffer. It uses
a free queue and full queue to syncup with the main process.
"""
positions = ['landlord', 'landlord_up', 'landlord_down']
try:
T = flags.unroll_length
log.info('Device %i Actor %i started.', device, i)
env = create_env(flags)
env = Environment(env, device)
done_buf = {p: [] for p in positions}
episode_return_buf = {p: [] for p in positions}
target_buf = {p: [] for p in positions}
obs_x_no_action_buf = {p: [] for p in positions}
obs_action_buf = {p: [] for p in positions}
obs_z_buf = {p: [] for p in positions}
size = {p: 0 for p in positions}
position, obs, env_output = env.initial()
while True:
while True:
obs_x_no_action_buf[position].append(env_output['obs_x_no_action'])
obs_z_buf[position].append(env_output['obs_z'])
with torch.no_grad():
agent_output = model.forward(position, obs['z_batch'], obs['x_batch'], flags=flags)
_action_idx = int(agent_output['action'].cpu().detach().numpy())
action = obs['legal_actions'][_action_idx]
obs_action_buf[position].append(_cards2tensor(action))
position, obs, env_output = env.step(action)
size[position] += 1
if env_output['done']:
for p in positions:
diff = size[p] - len(target_buf[p])
if diff > 0:
done_buf[p].extend([False for _ in range(diff-1)])
done_buf[p].append(True)
episode_return = env_output['episode_return'] if p == 'landlord' else -env_output['episode_return']
episode_return_buf[p].extend([0.0 for _ in range(diff-1)])
episode_return_buf[p].append(episode_return)
target_buf[p].extend([episode_return for _ in range(diff)])
break
for p in positions:
if size[p] > T:
index = free_queue[p].get()
if index is None:
break
for t in range(T):
buffers[p]['done'][index][t, ...] = done_buf[p][t]
buffers[p]['episode_return'][index][t, ...] = episode_return_buf[p][t]
buffers[p]['target'][index][t, ...] = target_buf[p][t]
buffers[p]['obs_x_no_action'][index][t, ...] = obs_x_no_action_buf[p][t]
buffers[p]['obs_action'][index][t, ...] = obs_action_buf[p][t]
buffers[p]['obs_z'][index][t, ...] = obs_z_buf[p][t]
full_queue[p].put(index)
done_buf[p] = done_buf[p][T:]
episode_return_buf[p] = episode_return_buf[p][T:]
target_buf[p] = target_buf[p][T:]
obs_x_no_action_buf[p] = obs_x_no_action_buf[p][T:]
obs_action_buf[p] = obs_action_buf[p][T:]
obs_z_buf[p] = obs_z_buf[p][T:]
size[p] -= T
except KeyboardInterrupt:
pass
except Exception as e:
log.error('Exception in worker process %i', i)
traceback.print_exc()
print()
raise e
def _cards2tensor(list_cards):
"""
Convert a list of integers to the tensor
representation
See Figure 2 in https://arxiv.org/pdf/2106.06135.pdf
"""
if len(list_cards) == 0:
return torch.zeros(54, dtype=torch.int8)
matrix = np.zeros([4, 13], dtype=np.int8)
jokers = np.zeros(2, dtype=np.int8)
counter = Counter(list_cards)
for card, num_times in counter.items():
if card < 20:
matrix[:, Card2Column[card]] = NumOnes2Array[num_times]
elif card == 20:
jokers[0] = 1
elif card == 30:
jokers[1] = 1
matrix = np.concatenate((matrix.flatten('F'), jokers))
matrix = torch.from_numpy(matrix)
return matrix

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douzero/evaluation/__init__.py Normal file
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import torch
import numpy as np
from douzero.env.env import get_obs
def _load_model(position, model_path):
from douzero.dmc.models import model_dict
model = model_dict[position]()
model_state_dict = model.state_dict()
if torch.cuda.is_available():
pretrained = torch.load(model_path, map_location='cuda:0')
else:
pretrained = torch.load(model_path, map_location='cpu')
pretrained = {k: v for k, v in pretrained.items() if k in model_state_dict}
model_state_dict.update(pretrained)
model.load_state_dict(model_state_dict)
if torch.cuda.is_available():
model.cuda()
model.eval()
return model
class DeepAgent:
def __init__(self, position, model_path):
self.model = _load_model(position, model_path)
def act(self, infoset):
# 只有一个合法动作时直接返回，这样会得不到胜率信息
# if len(infoset.legal_actions) == 1:
# return infoset.legal_actions[0], 0
obs = get_obs(infoset)
z_batch = torch.from_numpy(obs['z_batch']).float()
x_batch = torch.from_numpy(obs['x_batch']).float()
if torch.cuda.is_available():
z_batch, x_batch = z_batch.cuda(), x_batch.cuda()
y_pred = self.model.forward(z_batch, x_batch, return_value=True)['values']
y_pred = y_pred.detach().cpu().numpy()
best_action_index = np.argmax(y_pred, axis=0)[0]
best_action = infoset.legal_actions[best_action_index]
best_action_confidence = y_pred[best_action_index]
# print(best_action, best_action_confidence, y_pred)
return best_action, best_action_confidence

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import random
class RandomAgent():
def __init__(self):
self.name = 'Random'
def act(self, infoset):
return random.choice(infoset.legal_actions)

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import random
from rlcard.games.doudizhu.utils import CARD_TYPE
EnvCard2RealCard = {3: '3', 4: '4', 5: '5', 6: '6', 7: '7',
8: '8', 9: '9', 10: 'T', 11: 'J', 12: 'Q',
13: 'K', 14: 'A', 17: '2', 20: 'B', 30: 'R'}
RealCard2EnvCard = {'3': 3, '4': 4, '5': 5, '6': 6, '7': 7,
'8': 8, '9': 9, 'T': 10, 'J': 11, 'Q': 12,
'K': 13, 'A': 14, '2': 17, 'B': 20, 'R': 30}
INDEX = {'3': 0, '4': 1, '5': 2, '6': 3, '7': 4,
'8': 5, '9': 6, 'T': 7, 'J': 8, 'Q': 9,
'K': 10, 'A': 11, '2': 12, 'B': 13, 'R': 14}
class RLCardAgent(object):
def __init__(self, position):
self.name = 'RLCard'
self.position = position
def act(self, infoset):
try:
# Hand cards
hand_cards = infoset.player_hand_cards
for i, c in enumerate(hand_cards):
hand_cards[i] = EnvCard2RealCard[c]
hand_cards = ''.join(hand_cards)
# Last move
last_move = infoset.last_move.copy()
for i, c in enumerate(last_move):
last_move[i] = EnvCard2RealCard[c]
last_move = ''.join(last_move)
# Last two moves
last_two_cards = infoset.last_two_moves
for i in range(2):
for j, c in enumerate(last_two_cards[i]):
last_two_cards[i][j] = EnvCard2RealCard[c]
last_two_cards[i] = ''.join(last_two_cards[i])
# Last pid
last_pid = infoset.last_pid
action = None
# the rule of leading round
if last_two_cards[0] == '' and last_two_cards[1] == '':
chosen_action = None
comb = combine_cards(hand_cards)
min_card = hand_cards[0]
for _, acs in comb.items():
for ac in acs:
if min_card in ac:
chosen_action = ac
action = [char for char in chosen_action]
for i, c in enumerate(action):
action[i] = RealCard2EnvCard[c]
#print('lead action:', action)
# the rule of following cards
else:
the_type = CARD_TYPE[0][last_move][0][0]
chosen_action = ''
rank = 1000
for ac in infoset.legal_actions:
_ac = ac.copy()
for i, c in enumerate(_ac):
_ac[i] = EnvCard2RealCard[c]
_ac = ''.join(_ac)
if _ac != '' and the_type == CARD_TYPE[0][_ac][0][0]:
if int(CARD_TYPE[0][_ac][0][1]) < rank:
rank = int(CARD_TYPE[0][_ac][0][1])
chosen_action = _ac
if chosen_action != '':
action = [char for char in chosen_action]
for i, c in enumerate(action):
action[i] = RealCard2EnvCard[c]
#print('action:', action)
elif last_pid != 'landlord' and self.position != 'landlord':
action = []
if action is None:
action = random.choice(infoset.legal_actions)
except:
action = random.choice(infoset.legal_actions)
#import traceback
#traceback.print_exc()
assert action in infoset.legal_actions
return action
def card_str2list(hand):
hand_list = [0 for _ in range(15)]
for card in hand:
hand_list[INDEX[card]] += 1
return hand_list
def list2card_str(hand_list):
card_str = ''
cards = [card for card in INDEX]
for index, count in enumerate(hand_list):
card_str += cards[index] * count
return card_str
def pick_chain(hand_list, count):
chains = []
str_card = [card for card in INDEX]
hand_list = [str(card) for card in hand_list]
hand = ''.join(hand_list[:12])
chain_list = hand.split('0')
add = 0
for index, chain in enumerate(chain_list):
if len(chain) > 0:
if len(chain) >= 5:
start = index + add
min_count = int(min(chain)) // count
if min_count != 0:
str_chain = ''
for num in range(len(chain)):
str_chain += str_card[start+num]
hand_list[start+num] = int(hand_list[start+num]) - int(min(chain))
for _ in range(min_count):
chains.append(str_chain)
add += len(chain)
hand_list = [int(card) for card in hand_list]
return (chains, hand_list)
def combine_cards(hand):
'''Get optimal combinations of cards in hand
'''
comb = {'rocket': [], 'bomb': [], 'trio': [], 'trio_chain': [],
'solo_chain': [], 'pair_chain': [], 'pair': [], 'solo': []}
# 1. pick rocket
if hand[-2:] == 'BR':
comb['rocket'].append('BR')
hand = hand[:-2]
# 2. pick bomb
hand_cp = hand
for index in range(len(hand_cp) - 3):
if hand_cp[index] == hand_cp[index+3]:
bomb = hand_cp[index: index+4]
comb['bomb'].append(bomb)
hand = hand.replace(bomb, '')
# 3. pick trio and trio_chain
hand_cp = hand
for index in range(len(hand_cp) - 2):
if hand_cp[index] == hand_cp[index+2]:
trio = hand_cp[index: index+3]
if len(comb['trio']) > 0 and INDEX[trio[-1]] < 12 and (INDEX[trio[-1]]-1) == INDEX[comb['trio'][-1][-1]]:
comb['trio'][-1] += trio
else:
comb['trio'].append(trio)
hand = hand.replace(trio, '')
only_trio = []
only_trio_chain = []
for trio in comb['trio']:
if len(trio) == 3:
only_trio.append(trio)
else:
only_trio_chain.append(trio)
comb['trio'] = only_trio
comb['trio_chain'] = only_trio_chain
# 4. pick solo chain
hand_list = card_str2list(hand)
chains, hand_list = pick_chain(hand_list, 1)
comb['solo_chain'] = chains
# 5. pick par_chain
chains, hand_list = pick_chain(hand_list, 2)
comb['pair_chain'] = chains
hand = list2card_str(hand_list)
# 6. pick pair and solo
index = 0
while index < len(hand) - 1:
if hand[index] == hand[index+1]:
comb['pair'].append(hand[index] + hand[index+1])
index += 2
else:
comb['solo'].append(hand[index])
index += 1
if index == (len(hand) - 1):
comb['solo'].append(hand[index])
return comb

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from douzero.env.game import GameEnv
from .deep_agent import DeepAgent
EnvCard2RealCard = {3: '3', 4: '4', 5: '5', 6: '6', 7: '7',
8: '8', 9: '9', 10: 'T', 11: 'J', 12: 'Q',
13: 'K', 14: 'A', 17: '2', 20: 'X', 30: 'D'}
RealCard2EnvCard = {'3': 3, '4': 4, '5': 5, '6': 6, '7': 7,
'8': 8, '9': 9, 'T': 10, 'J': 11, 'Q': 12,
'K': 13, 'A': 14, '2': 17, 'X': 20, 'D': 30}
AllEnvCard = [3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7,
8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 12,
12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 17, 17, 17, 17, 20, 30]
def evaluate(landlord, landlord_up, landlord_down):
# 输入玩家的牌
user_hand_cards_real = input("请输入你的手牌, 例如 333456789TJQKA2XD:")
# user_hand_cards_real = "34666777899TJJKA22XD"
use_hand_cards_env = [RealCard2EnvCard[c] for c in list(user_hand_cards_real)]
# 输入玩家角色
user_position_code = int(input("请输入你的角色[0：地主上家, 1：地主, 2：地主下家]:"))
# user_position_code = 1
user_position = ['landlord_up', 'landlord', 'landlord_down'][user_position_code]
# 输入三张底牌
three_landlord_cards_real = input("请输入三张底牌, 例如 2XD:")
# three_landlord_cards_real = "2XD"
three_landlord_cards_env = [RealCard2EnvCard[c] for c in list(three_landlord_cards_real)]
# 整副牌减去玩家手上的牌，就是其他人的手牌,再分配给另外两个角色（如何分配对AI判断没有影响）
other_hand_cards = []
for i in set(AllEnvCard):
other_hand_cards.extend([i] * (AllEnvCard.count(i) - use_hand_cards_env.count(i)))
card_play_data_list = [{}]
card_play_data_list[0].update({
'three_landlord_cards': three_landlord_cards_env,
['landlord_up', 'landlord', 'landlord_down'][(user_position_code + 0) % 3]: use_hand_cards_env,
['landlord_up', 'landlord', 'landlord_down'][(user_position_code + 1) % 3]: other_hand_cards[0:17] if (user_position_code + 1) % 3 != 1 else other_hand_cards[17:],
['landlord_up', 'landlord', 'landlord_down'][(user_position_code + 2) % 3]: other_hand_cards[0:17] if (user_position_code + 1) % 3 == 1 else other_hand_cards[17:]
})
# 生成手牌结束，校验手牌数量
if len(card_play_data_list[0]["three_landlord_cards"]) != 3:
print("底牌必须是3张\n")
return
if len(card_play_data_list[0]["landlord_up"]) != 17 or \
len(card_play_data_list[0]["landlord_down"]) != 17 or \
len(card_play_data_list[0]["landlord"]) != 20:
print("初始手牌数目有误\n")
return
# print(card_play_data_list)
card_play_model_path_dict = {
'landlord': landlord,
'landlord_up': landlord_up,
'landlord_down': landlord_down}
print("创建代表玩家的AI...")
players = {}
players[user_position] = DeepAgent(user_position, card_play_model_path_dict[user_position])
env = GameEnv(players)
for idx, card_play_data in enumerate(card_play_data_list):
env.card_play_init(card_play_data)
print("开始出牌\n")
while not env.game_over:
env.step()
print("{}胜，本局结束!\n".format("农民" if env.winner == "farmer" else "地主"))
env.reset()

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# -*- coding: utf-8 -*-
# Created by: Raf
# Modify by: Vincentzyx
import GameHelper as gh
from GameHelper import GameHelper
import os
import sys
import time
import threading
import pyautogui
import win32gui
from PIL import Image
import multiprocessing as mp
from PyQt5 import QtGui, QtWidgets, QtCore
from PyQt5.QtWidgets import QGraphicsView, QGraphicsScene, QGraphicsItem, QGraphicsPixmapItem, QInputDialog, QMessageBox
from PyQt5.QtGui import QPixmap, QIcon
from PyQt5.QtCore import QTime, QEventLoop
from MainWindow import Ui_Form
from douzero.env.game import GameEnv
from douzero.evaluation.deep_agent import DeepAgent
import BidModel
import LandlordModel
import FarmerModel
EnvCard2RealCard = {3: '3', 4: '4', 5: '5', 6: '6', 7: '7',
8: '8', 9: '9', 10: 'T', 11: 'J', 12: 'Q',
13: 'K', 14: 'A', 17: '2', 20: 'X', 30: 'D'}
RealCard2EnvCard = {'3': 3, '4': 4, '5': 5, '6': 6, '7': 7,
'8': 8, '9': 9, 'T': 10, 'J': 11, 'Q': 12,
'K': 13, 'A': 14, '2': 17, 'X': 20, 'D': 30}
AllEnvCard = [3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7,
8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 12,
12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 17, 17, 17, 17, 20, 30]
AllCards = ['rD', 'bX', 'b2', 'r2', 'bA', 'rA', 'bK', 'rK', 'bQ', 'rQ', 'bJ', 'rJ', 'bT', 'rT',
'b9', 'r9', 'b8', 'r8', 'b7', 'r7', 'b6', 'r6', 'b5', 'r5', 'b4', 'r4', 'b3', 'r3']
helper = GameHelper()
helper.ScreenZoomRate = 1.25 # 请修改屏幕缩放比
class MyPyQT_Form(QtWidgets.QWidget, Ui_Form):
def __init__(self):
super(MyPyQT_Form, self).__init__()
self.setupUi(self)
self.setWindowFlags(QtCore.Qt.WindowMinimizeButtonHint | # 使能最小化按钮
QtCore.Qt.WindowCloseButtonHint) # 窗体总在最前端 QtCore.Qt.WindowStaysOnTopHint
self.setFixedSize(self.width(), self.height()) # 固定窗体大小
# self.setWindowIcon(QIcon('pics/favicon.ico'))
window_pale = QtGui.QPalette()
# window_pale.setBrush(self.backgroundRole(), QtGui.QBrush(QtGui.QPixmap("pics/bg.png")))
self.setPalette(window_pale)
self.Players = [self.RPlayer, self.Player, self.LPlayer]
self.counter = QTime()
# 参数
self.MyConfidence = 0.95 # 我的牌的置信度
self.OtherConfidence = 0.9 # 别人的牌的置信度
self.WhiteConfidence = 0.95 # 检测白块的置信度
self.LandlordFlagConfidence = 0.9 # # 检测地主标志的置信度
self.ThreeLandlordCardsConfidence = 0.9 # 检测地主底牌的置信度
self.PassConfidence = 0.85
self.WaitTime = 1 # 等待状态稳定延时
self.MyFilter = 40 # 我的牌检测结果过滤参数
self.OtherFilter = 25 # 别人的牌检测结果过滤参数
self.SleepTime = 0.1 # 循环中睡眠时间
self.RunGame = False
self.AutoPlay = False
# 坐标
self.MyHandCardsPos = (250, 764, 1141, 70) # 我的截图区域
self.LPlayedCardsPos = (463, 355, 380, 250) # 左边截图区域
self.RPlayedCardsPos = (946, 355, 380, 250) # 右边截图区域
self.LandlordFlagPos = [(1281, 276, 110, 140), (267, 695, 110, 140), (424, 237, 110, 140)] # 地主标志截图区域(右-我-左)
self.ThreeLandlordCardsPos = (763, 37, 287, 136) # 地主底牌截图区域，resize成349x168
self.PassBtnPos = (686, 659, 419, 100)
self.GeneralBtnPos = (616, 631, 576, 117)
# 信号量
self.shouldExit = 0 # 通知上一轮记牌结束
self.canRecord = threading.Lock() # 开始记牌
self.card_play_model_path_dict = {
'landlord': "baselines/douzero_ADP/landlord.ckpt",
'landlord_up': "baselines/douzero_ADP/landlord_up.ckpt",
'landlord_down': "baselines/douzero_ADP/landlord_down.ckpt"
}
# cards = self.find_three_landlord_cards(self.ThreeLandlordCardsPos)
# print(cards)
# exit()
def init_display(self):
self.WinRate.setText("评分")
self.InitCard.setText("开始")
self.UserHandCards.setText("手牌")
self.LPlayedCard.setText("上家出牌区域")
self.RPlayedCard.setText("下家出牌区域")
self.PredictedCard.setText("AI出牌区域")
self.ThreeLandlordCards.setText("地主牌")
self.SwitchMode.setText("自动" if self.AutoPlay else "单局")
for player in self.Players:
player.setStyleSheet('background-color: rgba(255, 0, 0, 0);')
def switch_mode(self):
self.AutoPlay = not self.AutoPlay
self.SwitchMode.setText("自动" if self.AutoPlay else "单局")
def init_cards(self):
self.RunGame = True
GameHelper.Interrupt = False
self.init_display()
# 玩家手牌
self.user_hand_cards_real = ""
self.user_hand_cards_env = []
# 其他玩家出牌
self.other_played_cards_real = ""
self.other_played_cards_env = []
# 其他玩家手牌（整副牌减去玩家手牌，后续再减掉历史出牌）
self.other_hand_cards = []
# 三张底牌
self.three_landlord_cards_real = ""
self.three_landlord_cards_env = []
# 玩家角色代码：0-地主上家, 1-地主, 2-地主下家
self.user_position_code = None
self.user_position = ""
# 开局时三个玩家的手牌
self.card_play_data_list = {}
# 出牌顺序：0-玩家出牌, 1-玩家下家出牌, 2-玩家上家出牌
self.play_order = 0
self.env = None
# 识别玩家手牌
self.user_hand_cards_real = self.find_my_cards(self.MyHandCardsPos)
self.UserHandCards.setText(self.user_hand_cards_real)
self.user_hand_cards_env = [RealCard2EnvCard[c] for c in list(self.user_hand_cards_real)]
# 识别三张底牌
self.three_landlord_cards_real = self.find_three_landlord_cards(self.ThreeLandlordCardsPos)
self.ThreeLandlordCards.setText("底牌：" + self.three_landlord_cards_real)
self.three_landlord_cards_env = [RealCard2EnvCard[c] for c in list(self.three_landlord_cards_real)]
for testCount in range(1, 5):
if len(self.three_landlord_cards_env) > 3:
self.ThreeLandlordCardsConfidence += 0.05
elif len(self.three_landlord_cards_env) < 3:
self.ThreeLandlordCardsConfidence -= 0.05
else:
break
self.three_landlord_cards_real = self.find_three_landlord_cards(self.ThreeLandlordCardsPos)
self.ThreeLandlordCards.setText("底牌：" + self.three_landlord_cards_real)
self.three_landlord_cards_env = [RealCard2EnvCard[c] for c in list(self.three_landlord_cards_real)]
# 识别玩家的角色
self.user_position_code = self.find_landlord(self.LandlordFlagPos)
if self.user_position_code is None:
items = ("地主上家", "地主", "地主下家")
item, okPressed = QInputDialog.getItem(self, "选择角色", "未识别到地主，请手动选择角色:", items, 0, False)
if okPressed and item:
self.user_position_code = items.index(item)
else:
return
self.user_position = ['landlord_up', 'landlord', 'landlord_down'][self.user_position_code]
for player in self.Players:
player.setStyleSheet('background-color: rgba(255, 0, 0, 0);')
self.Players[self.user_position_code].setStyleSheet('background-color: rgba(255, 0, 0, 0.1);')
# 整副牌减去玩家手上的牌，就是其他人的手牌,再分配给另外两个角色（如何分配对AI判断没有影响）
for i in set(AllEnvCard):
self.other_hand_cards.extend([i] * (AllEnvCard.count(i) - self.user_hand_cards_env.count(i)))
self.card_play_data_list.update({
'three_landlord_cards': self.three_landlord_cards_env,
['landlord_up', 'landlord', 'landlord_down'][(self.user_position_code + 0) % 3]:
self.user_hand_cards_env,
['landlord_up', 'landlord', 'landlord_down'][(self.user_position_code + 1) % 3]:
self.other_hand_cards[0:17] if (self.user_position_code + 1) % 3 != 1 else self.other_hand_cards[17:],
['landlord_up', 'landlord', 'landlord_down'][(self.user_position_code + 2) % 3]:
self.other_hand_cards[0:17] if (self.user_position_code + 1) % 3 == 1 else self.other_hand_cards[17:]
})
print("开始对局")
print("手牌:",self.user_hand_cards_real)
print("地主牌:",self.three_landlord_cards_real)
# 生成手牌结束，校验手牌数量
if len(self.card_play_data_list["three_landlord_cards"]) != 3:
QMessageBox.critical(self, "底牌识别出错", "底牌必须是3张！", QMessageBox.Yes, QMessageBox.Yes)
self.init_display()
return
if len(self.card_play_data_list["landlord_up"]) != 17 or \
len(self.card_play_data_list["landlord_down"]) != 17 or \
len(self.card_play_data_list["landlord"]) != 20:
QMessageBox.critical(self, "手牌识别出错", "初始手牌数目有误", QMessageBox.Yes, QMessageBox.Yes)
self.init_display()
return
# 得到出牌顺序
self.play_order = 0 if self.user_position == "landlord" else 1 if self.user_position == "landlord_up" else 2
# 创建一个代表玩家的AI
ai_players = [0, 0]
ai_players[0] = self.user_position
ai_players[1] = DeepAgent(self.user_position, self.card_play_model_path_dict[self.user_position])
self.env = GameEnv(ai_players)
try:
self.start()
except:
self.stop()
def sleep(self, ms):
self.counter.restart()
while self.counter.elapsed() < ms:
QtWidgets.QApplication.processEvents(QEventLoop.AllEvents, 50)
def start(self):
self.env.card_play_init(self.card_play_data_list)
print("开始出牌\n")
while not self.env.game_over:
# 玩家出牌时就通过智能体获取action，否则通过识别获取其他玩家出牌
if self.play_order == 0:
self.PredictedCard.setText("...")
action_message = self.env.step(self.user_position)
# 更新界面
self.UserHandCards.setText("手牌：" + str(''.join(
[EnvCard2RealCard[c] for c in self.env.info_sets[self.user_position].player_hand_cards]))[::-1])
self.PredictedCard.setText(action_message["action"] if action_message["action"] else "不出")
self.WinRate.setText("评分：" + action_message["win_rate"])
print("\n手牌：", str(''.join(
[EnvCard2RealCard[c] for c in self.env.info_sets[self.user_position].player_hand_cards])))
print("出牌：", action_message["action"] if action_message["action"] else "不出", "， 胜率：",
action_message["win_rate"])
if action_message["action"] == "":
helper.ClickOnImage("pass_btn", region=self.PassBtnPos)
else:
helper.SelectCards(action_message["action"])
tryCount = 20
result = helper.LocateOnScreen("play_card", region=self.PassBtnPos, confidence=0.85)
while result is None and tryCount > 0:
if not self.RunGame:
break
print("等待出牌按钮")
self.detect_start_btn()
tryCount -= 1
result = helper.LocateOnScreen("play_card", region=self.PassBtnPos, confidence=0.85)
self.sleep(100)
helper.ClickOnImage("play_card", region=self.PassBtnPos, confidence=0.85)
self.sleep(2200)
self.detect_start_btn()
self.play_order = 1
elif self.play_order == 1:
self.RPlayedCard.setText("...")
pass_flag = helper.LocateOnScreen('pass',
region=self.RPlayedCardsPos,
confidence=self.PassConfidence)
self.detect_start_btn()
while self.RunGame and self.have_white(self.RPlayedCardsPos) == 0 and pass_flag is None:
print("等待下家出牌")
self.sleep(100)
pass_flag = helper.LocateOnScreen('pass', region=self.RPlayedCardsPos,
confidence=self.PassConfidence)
self.detect_start_btn()
self.sleep(200)
# 未找到"不出"
if pass_flag is None:
# 识别下家出牌
self.RPlayedCard.setText("等待动画")
self.sleep(1200)
self.RPlayedCard.setText("识别中")
self.other_played_cards_real = self.find_other_cards(self.RPlayedCardsPos)
print("下家出牌", self.other_played_cards_real)
self.sleep(500)
# 找到"不出"
else:
self.other_played_cards_real = ""
print("\n下家出牌：", self.other_played_cards_real)
self.other_played_cards_env = [RealCard2EnvCard[c] for c in list(self.other_played_cards_real)]
self.env.step(self.user_position, self.other_played_cards_env)
# 更新界面
self.RPlayedCard.setText(self.other_played_cards_real if self.other_played_cards_real else "不出")
self.play_order = 2
self.sleep(500)
elif self.play_order == 2:
self.LPlayedCard.setText("...")
self.detect_start_btn()
pass_flag = helper.LocateOnScreen('pass', region=self.LPlayedCardsPos,
confidence=self.PassConfidence)
while self.RunGame and self.have_white(self.LPlayedCardsPos) == 0 and pass_flag is None:
print("等待上家出牌")
self.detect_start_btn()
self.sleep(100)
pass_flag = helper.LocateOnScreen('pass', region=self.LPlayedCardsPos,
confidence=self.PassConfidence)
self.sleep(200)
# 不出
# 未找到"不出"
if pass_flag is None:
# 识别上家出牌
self.LPlayedCard.setText("等待动画")
self.sleep(1200)
self.LPlayedCard.setText("识别中")
self.other_played_cards_real = self.find_other_cards(self.LPlayedCardsPos)
# 找到"不出"
else:
self.other_played_cards_real = ""
print("\n上家出牌：", self.other_played_cards_real)
self.other_played_cards_env = [RealCard2EnvCard[c] for c in list(self.other_played_cards_real)]
self.env.step(self.user_position, self.other_played_cards_env)
self.play_order = 0
# 更新界面
self.LPlayedCard.setText(self.other_played_cards_real if self.other_played_cards_real else "不出")
self.sleep(500)
else:
pass
self.sleep(100)
print("{}胜，本局结束!\n".format("农民" if self.env.winner == "farmer" else "地主"))
# QMessageBox.information(self, "本局结束", "{}胜！".format("农民" if self.env.winner == "farmer" else "地主"),
# QMessageBox.Yes, QMessageBox.Yes)
self.detect_start_btn()
def find_landlord(self, landlord_flag_pos):
for pos in landlord_flag_pos:
result = helper.LocateOnScreen("landlord_words", region=pos,
confidence=self.LandlordFlagConfidence)
if result is not None:
return landlord_flag_pos.index(pos)
return None
def detect_start_btn(self):
result = helper.LocateOnScreen("change_player_btn", region=(667, 741, 934, 404))
if result is not None:
self.RunGame = False
self.stop()
result = helper.LocateOnScreen("yes_btn", region=(680, 661, 435, 225))
if result is not None:
helper.ClickOnImage("yes_btn", region=(680, 661, 435, 225))
self.sleep(1000)
result = helper.LocateOnScreen("get_award_btn", region=(680, 661, 435, 225))
if result is not None:
helper.ClickOnImage("get_award_btn", region=(680, 661, 435, 225))
self.sleep(1000)
result = helper.LocateOnScreen("yes_btn_sm", region=(669, 583, 468, 100))
if result is not None:
helper.ClickOnImage("yes_btn_sm", region=(669, 583, 468, 100))
self.sleep(200)
def find_three_landlord_cards(self, pos):
img, _ = helper.Screenshot()
img = img.crop((pos[0], pos[1], pos[0] + pos[2], pos[1] + pos[3]))
img = img.resize((349, 168))
three_landlord_cards_real = ""
for card in AllCards:
result = pyautogui.locateAll(needleImage=helper.Pics['o' + card], haystackImage=img,
confidence=self.ThreeLandlordCardsConfidence)
three_landlord_cards_real += card[1] * self.cards_filter(list(result), self.OtherFilter)
if len(three_landlord_cards_real) > 3:
three_landlord_cards_real = ""
for card in AllCards:
result = pyautogui.locateAll(needleImage=helper.Pics['o' + card], haystackImage=img,
confidence=self.ThreeLandlordCardsConfidence + 0.05)
three_landlord_cards_real += card[1] * self.cards_filter(list(result), self.OtherFilter)
if len(three_landlord_cards_real) < 3:
three_landlord_cards_real = ""
for card in AllCards:
result = pyautogui.locateAll(needleImage=helper.Pics['o' + card], haystackImage=img,
confidence=self.ThreeLandlordCardsConfidence + 0.1)
three_landlord_cards_real += card[1] * self.cards_filter(list(result), self.OtherFilter)
return three_landlord_cards_real
def find_my_cards(self, pos):
user_hand_cards_real = ""
img, _ = helper.Screenshot()
cards, _ = helper.GetCards(img)
for c in cards:
user_hand_cards_real += c[0]
# for card in AllCards:
# result = pyautogui.locateAll(needleImage=helper.Pics['m'+card], haystackImage=img, confidence=self.MyConfidence)
# user_hand_cards_real += card[1] * self.cards_filter(list(result), self.MyFilter)
return user_hand_cards_real
def find_other_cards(self, pos):
other_played_cards_real = ""
self.sleep(500)
img, _ = helper.Screenshot(region=pos)
for card in AllCards:
result = pyautogui.locateAll(needleImage=helper.Pics['o' + card], haystackImage=img,
confidence=self.OtherConfidence)
other_played_cards_real += card[1] * self.cards_filter(list(result), self.OtherFilter)
return other_played_cards_real
def cards_filter(self, location, distance): # 牌检测结果滤波
if len(location) == 0:
return 0
locList = [location[0][0]]
count = 1
for e in location:
flag = 1 # “是新的”标志
for have in locList:
if abs(e[0] - have) <= distance:
flag = 0
break
if flag:
count += 1
locList.append(e[0])
return count
def have_white(self, pos): # 是否有白块
img, _ = helper.Screenshot()
result = pyautogui.locate(needleImage=helper.Pics["white"], haystackImage=img,
region=pos, confidence=self.WhiteConfidence)
if result is None:
return 0
else:
return 1
def stop(self):
try:
self.RunGame = False
self.env.game_over = True
self.env.reset()
self.init_display()
self.PreWinrate.setText("局前预估胜率：")
self.BidWinrate.setText("叫牌预估胜率：")
except AttributeError as e:
pass
if self.AutoPlay:
play_btn = helper.LocateOnScreen("change_player_btn", region=(667, 741, 934, 404))
while play_btn is None and self.AutoPlay:
play_btn = helper.LocateOnScreen("change_player_btn", region=(667, 741, 934, 404))
self.sleep(100)
if play_btn is not None:
helper.LeftClick((play_btn[0], play_btn[1]))
self.beforeStart()
def beforeStart(self):
GameHelper.Interrupt = True
thresholds = [
[75, 60],
[85, 70]
]
while True:
outterBreak = False
jiaodizhu_btn = helper.LocateOnScreen("jiaodizhu_btn", region=(765, 663, 116, 50))
qiangdizhu_btn = helper.LocateOnScreen("qiangdizhu_btn", region=(783, 663, 116, 50))
jiabei_btn = helper.LocateOnScreen("jiabei_btn", region=self.GeneralBtnPos)
self.detect_start_btn()
while jiaodizhu_btn is None and qiangdizhu_btn is None and jiabei_btn is None:
self.detect_start_btn()
print("等待加倍或叫地主")
self.sleep(100)
jiaodizhu_btn = helper.LocateOnScreen("jiaodizhu_btn", region=(765, 663, 116, 50))
qiangdizhu_btn = helper.LocateOnScreen("qiangdizhu_btn", region=(783, 663, 116, 50))
jiabei_btn = helper.LocateOnScreen("jiabei_btn", region=self.GeneralBtnPos)
if jiabei_btn is None:
img, _ = helper.Screenshot()
cards, _ = helper.GetCards(img)
cards_str = "".join([card[0] for card in cards])
win_rate = BidModel.predict(cards_str)
print("预计叫地主胜率：", win_rate)
self.BidWinrate.setText("叫牌预估胜率：" + str(round(win_rate, 2)) + "%")
is_stolen = 0
if jiaodizhu_btn is not None:
if win_rate > 55:
helper.ClickOnImage("jiaodizhu_btn", region=(765, 663, 116, 50), confidence=0.9)
else:
helper.ClickOnImage("bujiao_btn", region=self.GeneralBtnPos)
elif qiangdizhu_btn is not None:
is_stolen = 1
if win_rate > 60:
helper.ClickOnImage("qiangdizhu_btn", region=(783, 663, 116, 50), confidence=0.9)
else:
helper.ClickOnImage("buqiang_btn", region=self.GeneralBtnPos)
else:
pass
else:
llcards = self.find_three_landlord_cards(self.ThreeLandlordCardsPos)
print("地主牌:", llcards)
img, _ = helper.Screenshot()
cards, _ = helper.GetCards(img)
cards_str = "".join([card[0] for card in cards])
if len(cards_str) == 20:
win_rate = LandlordModel.predict(cards_str)
self.PreWinrate.setText("局前预估胜率：" + str(round(win_rate, 2)) + "%")
print("预估地主胜率:", win_rate)
else:
user_position_code = self.find_landlord(self.LandlordFlagPos)
user_position = "up"
while user_position_code is None:
user_position_code = self.find_landlord(self.LandlordFlagPos)
self.sleep(50)
user_position = ['up', 'landlord', 'down'][user_position_code]
win_rate = FarmerModel.predict(cards_str, llcards, user_position) - 5
print("预估农民胜率:", win_rate)
self.PreWinrate.setText("局前预估胜率：" + str(round(win_rate, 2)) + "%")
if win_rate > thresholds[is_stolen][0]:
chaojijiabei_btn = helper.LocateOnScreen("chaojijiabei_btn", region=self.GeneralBtnPos)
if chaojijiabei_btn is not None:
helper.ClickOnImage("chaojijiabei_btn", region=self.GeneralBtnPos)
else:
helper.ClickOnImage("jiabei_btn", region=self.GeneralBtnPos)
elif win_rate > thresholds[is_stolen][1]:
helper.ClickOnImage("jiabei_btn", region=self.GeneralBtnPos)
else:
helper.ClickOnImage("bujiabei_btn", region=self.GeneralBtnPos)
outterBreak = True
break
if outterBreak:
break
llcards = self.find_three_landlord_cards(self.ThreeLandlordCardsPos)
while len(llcards) != 3:
print("等待地主牌", llcards)
self.sleep(100)
llcards = self.find_three_landlord_cards(self.ThreeLandlordCardsPos)
self.sleep(4000)
self.init_cards()
if __name__ == '__main__':
app = QtWidgets.QApplication(sys.argv)
app.setStyleSheet("""
QPushButton{
text-align : center;
background-color : white;
font: bold;
border-color: gray;
border-width: 2px;
border-radius: 10px;
padding: 6px;
height : 14px;
border-style: outset;
font : 14px;
}
QPushButton:hover{
background-color : light gray;
}
QPushButton:pressed{
text-align : center;
background-color : gray;
font: bold;
border-color: gray;
border-width: 2px;
border-radius: 10px;
padding: 6px;
height : 14px;
border-style: outset;
font : 14px;
padding-left:9px;
padding-top:9px;
}
QComboBox{
background:transparent;
border: 1px solid rgba(200, 200, 200, 100);
font-weight: bold;
}
QComboBox:drop-down{
border: 0px;
}
QComboBox QAbstractItemView:item{
height: 30px;
}
QLabel{
background:transparent;
font-weight: bold;
}
""")
my_pyqt_form = MyPyQT_Form()
my_pyqt_form.show()
sys.exit(app.exec_())

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