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auto_play.py
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import numpy as np
import time
from copy import deepcopy
import pyautogui
from alpha_beta_search import Min_Max_Search
from vc import VC
from functions import bxy2sxy, sxy2bxy
pyautogui.FAILSAFE = True # 自启动故障处理
if __name__ == '__main__':
# 初始化参数与变量
board_length = 385
b = np.zeros((15, 15)).astype(int)
row_s = np.zeros(len(b)).astype(int)
col_s = np.zeros(len(b)).astype(int)
diag_s = np.zeros(2 * len(b) - 1).astype(int)
t_diag_s = np.zeros(2 * len(b) - 1).astype(int)
search_machine = Min_Max_Search(2)
hashing_v = search_machine.zobrist.init_hashing_value
# 定位棋盘
loc = pyautogui.locateCenterOnScreen("images/icon0.png", confidence=0.8)
region = (loc.x - 207, loc.y + 213, loc.x + 207, loc.y + 645)
lu_x, lu_y = loc.x - 193, loc.y + 227
region2 = (loc.x - 207, loc.y + 645, loc.x + 207, loc.y + 845)
# 对战局数
epoch = 10
while epoch > 0:
print(f'----epoch{epoch}----')
epoch -= 1
flag = pyautogui.locateCenterOnScreen("images/icon5.png", confidence=0.8, region=region2) # 游戏是否结束
chess_flag = None
loc1, loc2 = None, None
while flag is None:
# 判断AI执哪种棋
while chess_flag is None and loc1 is None and loc2 is None:
loc1 = pyautogui.locateCenterOnScreen("images/icon3.png", confidence=0.8,
region=region2) # region参数限制查找范围，加快查找速度
loc2 = pyautogui.locateCenterOnScreen("images/icon4.png", confidence=0.8,
region=region2) # region参数限制查找范围，加快查找速度
if loc1 is not None:
chess_flag = 2
n_chess = 'images/icon2.png'
else:
chess_flag = 1
n_chess = 'images/icon1.png'
vc_machine = VC(3 - chess_flag)
trans_vc_machine = VC(chess_flag)
# 先手下中间
if chess_flag == 2 and np.sum(b) == 0:
w_x, w_y = 7, 7
b[w_x][w_y] = 1
hashing_v = search_machine.zobrist.calculate_hashing_value(hashing_v, w_x, w_y, 0, 1)
pyautogui.click(*bxy2sxy(w_x, w_y, board_length, lu_x, lu_y), clicks=2)
# 获取对方下棋的坐标
n_x, n_y = None, None
# 等待对方下棋
while n_x is None:
tmp_res = pyautogui.locateCenterOnScreen(n_chess, confidence=0.8,
region=region)
if tmp_res is not None:
n_x, n_y = tmp_res
# 判断对方是否投降
flag = pyautogui.locateCenterOnScreen("images/icon5.png", confidence=0.8, region=region2)
if flag is not None:
break
if flag is not None:
break
# 对方落子
pyautogui.moveTo(n_x, n_y)
x, y = sxy2bxy(n_x, n_y, board_length, lu_x, lu_y)
b[x][y] = chess_flag
hashing_v = search_machine.zobrist.calculate_hashing_value(hashing_v, x, y, 0, chess_flag)
_, row_s, col_s, diag_s, t_diag_s = search_machine.eval_machine.evaluate(b, row_s, col_s,
diag_s, t_diag_s,
x, y)
# AI落子
t1 = time.time_ns()
vc_f, w_x, w_y = vc_machine.vc_search(3 - chess_flag, int(x), int(y), b, np.fliplr(b), 5, True)
if vc_f is False:
_, w_x, w_y, positions = search_machine.search(b, row_s, col_s, diag_s, t_diag_s, 0,
3 - chess_flag,
(False if chess_flag == 1 else True),
-10000000000,
10000000000, deepcopy(hashing_v))
positions = sorted(positions, key=lambda x: x[2])
for w_x, w_y, _s in positions:
b[w_x][w_y] = 3 - chess_flag
d_vc_f, _, _ = trans_vc_machine.vc_search(chess_flag, w_x, w_y, b, np.fliplr(b), 5,
True)
if d_vc_f:
b[w_x][w_y] = 0
print('trying to defend')
else:
print('defended')
break
else:
print('vc yes!')
t2 = time.time_ns()
b[w_x][w_y] = 3 - chess_flag
hashing_v = search_machine.zobrist.calculate_hashing_value(hashing_v, w_x, w_y, 0,
3 - chess_flag)
_, row_s, col_s, diag_s, t_diag_s = search_machine.eval_machine.evaluate(b, row_s, col_s,
diag_s,
t_diag_s, w_x, w_y)
print('searching time:', (t2 - t1) / 1e9, ' ', 'eval_val:', _)
pyautogui.click(*bxy2sxy(w_x, w_y, board_length, lu_x, lu_y), clicks=2)
b = np.zeros((15, 15)).astype(int)
row_s = np.zeros(len(b)).astype(int)
col_s = np.zeros(len(b)).astype(int)
diag_s = np.zeros(2 * len(b) - 1).astype(int)
t_diag_s = np.zeros(2 * len(b) - 1).astype(int)
hashing_v = search_machine.zobrist.init_hashing_value
# 保存数据
# save_variable(search_machine.eval_machine.eval_dict, r'eval_memory.pkl')
if epoch > 0:
# 离开
tmp_loc = pyautogui.locateCenterOnScreen("images/icon6.png", confidence=0.8, region=region2)
pyautogui.click(*tmp_loc)
# 开始
tmp_loc = pyautogui.locateCenterOnScreen("images/icon7.png", confidence=0.8, region=region)
pyautogui.click(*tmp_loc)