def check_hook(self):
pos = self.pos
key_dict = self.key_dict
pic = cpic.pic()
npc = []
for i in range(1, 4):
ptemp = cpic.pic(' ')
ptemp.img = pic.getslice(
slice=pos.get_rio(key_dict['np%d' % i], width=20))
npc.append(ptemp)
time.sleep(0.2)
npp = cpic.pic()
for i in range(1, 4):
np = npp.getslice(slice=pos.get_rio(key_dict['np%d' %
i], width=20))
cmp = npc[i - 1].compare(np)
print(cmp)
if cmp == 1.0:
print("error hook state!!!")
return self.check_hook()
if cmp < 0.7:
self.hook[i - 1] = 1
else:
self.hook[i - 1] = 0
print("hook state:", self.hook)
return 0
def log_skill(self):
if len(self.skill_img) > 1:
return
img = cpic.pic()
st = self.key_dict['sk_st']
pos = self.pos
self.skill_img = []
for i in range(st, st + 9):
pic = cpic.pic(' ')
pic.img = img.getslice(pos.get_rio(i, width=20))
pic.save('pic/skill/%d.bmp' % i)
self.skill_img.append(pic.img)
def skill_cold_check(self):
curp = cpic.pic()
cursk = cpic.pic(' ')
st = self.key_dict['sk_st']
pos = self.pos
if len(self.skill_img) < 9:
return
for i in range(st, st + 9):
cursk.img = curp.getslice(pos.get_rio(i, width=20))
# sim = cursk.compare(self.skill_img[i - st])
# print("sk", i, ":", sim)
if cursk.compare(self.skill_img[i - st]) < 0.95:
self.skill_cdwn[i - st] = 0 # 不可使用
else:
self.skill_cdwn[i - st] = 1 # 可使用
def log_face(self):
img = cpic.pic()
temp = cpic.pic(' ')
st = self.key_dict['face_st']
pos = self.pos
self.skill_img = []
ans = input()
ans = ans.split(' ')
mesh = {'0': 'b', '1': 'q', '2': 'a'}
for i in range(st, st + 5):
temp.img = img.getslice(pos.get_rio(i))
tp = mesh[ans[i - st]]
tp += str(time.time())[-6:]
temp.save('pic/train_face/%s.bmp' % tp)
print("face log!")
def cardcolor(self):
st = self.key_dict['card_st']
pic = cpic.pic()
pos = self.pos
for i in range(st, st + 5):
self.card[i - st] = self.clf.judge(
pic.getslice(slice=pos.get_rio(i, width=70)))
print('card color:', self.card)
def cardface(self):
st = self.key_dict['face_st']
pos = self.pos
pic = cpic.pic()
ids = {'b': 0, 'q': 1, 'a': 2}
for i in range(st, st + 5):
self.face[i - st] = ids[self.clf_face.judge(
pic.getslice(slice=pos.get_rio(i)))]
print('card actor:', self.face)
def detect(self, fn, w=50, thr=0.9, dtype='judg'):
pos = self.pos
fname = fn + '.bmp'
idp = self.key_dict[fn]
try:
thr = self.thr[fn]
except KeyError:
pass
pic_cmp = cpic.pic(fname)
pic = cpic.pic(' ')
pic.img = self.curpic.getslice(slice=pos.get_rio(idp, w))
how = pic_cmp.compare(pic)
if dtype == 'how':
return how
elif how < thr:
return False
else:
print('detect-:', fn, ' !')
return True
def out_skill(self, actor, sknum):
if actor > 0:
actor -= 1
sknum -= 1
skid = 3 * actor + sknum
if self.skill_cdwn[skid] == 0:
print("skill", skid, "is colding..")
return
self.pos.click(skid)
time.sleep(1)
self.curpic = cpic.pic()
self.detect_and_wait('attack')
else:
st = self.key_dict['master_sk']
self.pos.click(st)
time.sleep(0.7)
self.pos.click(st + sknum)
time.sleep(1)
self.curpic = cpic.pic()
self.detect_and_wait('attack')
def detect_and_wait(self, fn, thr=0.9, w=50, jud=False):
ct = 0
while self.detect(fn, w, thr, dtype='judg') == jud and ct < 68:
time.sleep(1)
self.curpic = cpic.pic()
if ct >= 5 and ct % 5 == 0:
pass # self.click('enm_blank')
if ct == 66:
os.system('shutdown -s -t 30')
# pygame.mixer.init()
# pygame.mixer.music.load('reminder.mp3')
# pygame.mixer.music.play()
input()
pygame.mixer.music.stop()
ct += 1
def check_turn(self):
# 回合数检查
print("checking...")
p1 = self.scene1
p2 = self.scene2
p3 = self.scene3
score = np.array([0., 0., 0.])
for i in range(3):
p = cpic.pic(
slice=self.pos.get_rio(self.key_dict['scene'], width=20))
score += np.array([p.compare(p1), p.compare(p2), p.compare(p3)])
time.sleep(0.2)
turn = np.argmax(score) + 1
if 0 <= turn - self.turn <= 1:
self.turn = turn
print("new turn:", self.turn)
else:
print("wrong turn:", turn)
# 宝具检查：
self.check_hook()
print("checking done")
def get_new_pic(self):
time.sleep(1)
self.curpic = cpic.pic()
def detect_and_wait_with_click(self, fn, dst, thr=0.9, w=50, jud=False):
ct = 5
while self.detect(fn, w, thr, dtype='judg') == jud and ct:
self.click(dst)
self.curpic = cpic.pic()
ct -= 1
# mfcDC创建可兼容的DC
saveDC = mfcDC.CreateCompatibleDC()
# 创建bigmap准备保存图片
saveBitMap = win32ui.CreateBitmap()
# 获取监控器信息
MoniterDev = win32api.EnumDisplayMonitors(None, None)
w = MoniterDev[0][2][2]
h = MoniterDev[0][2][3]
# print w,h #图片大小
# 为bitmap开辟空间
saveBitMap.CreateCompatibleBitmap(mfcDC, w, h)
# 高度saveDC，将截图保存到saveBitmap中
saveDC.SelectObject(saveBitMap)
# 截取从左上角（0，0）长宽为（w，h）的图片
saveDC.BitBlt((0, 0), (w, h), mfcDC, (0, 0), win32con.SRCCOPY)
saveBitMap.SaveBitmapFile(saveDC, filename)
import time
from Wind import cpic
st = time.time()
p = cpic.pic()
ed = time.time()
print("1:", time.time() - st)
wind_capture(u'劲风版01')
print("2:", time.time() - ed)
def __init__(self, t=1):
self.cur = 0
self.turn = 1
self.state = 0
self.hook = [0, 0, 0]
self.card = ['', '', '', '', '']
self.face = ['', '', '', '', '']
self.clf = SVM.SVM_clf() # 卡色训练识别
self.clf_face = SVM.SVM_clf() # 脸谱训练识别
if t:
self.clf.train('pic/train')
self.clf_face.train('pic/train_face')
self.pos = cpos.pos()
self.key_dict = {
'fb': 48,
'zhuzhan': 40,
'start': 41,
'attack': 15,
'btres': 38,
'swc_spd': 25,
'next': 37,
'scene': 54,
'card_st': 49,
'hook_st': 34,
'np1': 10,
'np2': 12,
'np3': 14,
'apple': 43,
'apple_ok': 44,
'retblank': 55,
'master_sk': 16,
'sk_st': 0,
'face_st': 20,
'manpo': 56,
'flush': 57,
'flush_ok': 58,
'enm_blank': 28,
'chouj': 59,
'chongzhi': 60,
'chongzhi_ok': 61,
'chongzhi_done': 62
}
self.thr = {
'next': 0.7,
'attack': 0.3,
'btres': 0.2,
'swc_spd': 0.2,
'manpo': 0.9
}
self.skill_img = []
for i in range(0, 9):
fname = 'skill/%d.bmp' % i
pic = cpic.pic(fname)
self.skill_img.append(pic)
self.skill_cdwn = [1, 1, 1, 1, 1, 1, 1, 1, 1]
self.scene1 = cpic.pic('scene1.bmp')
self.scene2 = cpic.pic('scene2.bmp')
self.scene3 = cpic.pic('scene3.bmp')
self.count_time = time.time()
self.curpic = None # use for detect
from ML import SVM
from Wind import cpic
from Wind import cpos
import time
if __name__ == "__main__":
clf = SVM.SVM_clf()
pos = cpos.pos()
st = 49
picfile = 'pic/train'
while 1:
clf.train(picfile)
pics = []
pic_ = cpic.pic()
for i in range(st, st + 5):
pic = cpic.pic(' ')
pic.img = pic_.getslice(pos.get_rio(i))
print(clf.judge(pic))
pics.append(pic)
ctrl = input()
ans = ctrl.split(' ')
for i in range(5):
pics[i].save(picfile + '/' + ans[i].strip() + '%s.bmp' %
(str(time.time())[-7:]))
pos = cpos.pos('scene/pos.txt')
while 1:
cmd = input()
cmds = cmd.split()
ctr = cmds[0]
i = int(cmds[1])
w = 50
if len(cmds) > 2:
w = int(cmds[2])
if ctr == 's':
pic = None
if i >= 0:
pic = cpic.pic(slice=pos.get_rio(i, width=w))
else:
pic = cpic.pic()
pic.show()
elif ctr == 'c':
pos.click(i)
elif ctr == 'sc':
f = open('clog.txt', 'a')
f.write(i)
f.write('\n')
f.close()
elif ctr == 'sp': # save pic
pic = None
if int(i) >= 0:
pic = cpic.pic(slice=pos.get_rio(i, width=w))
else:
def train(self,fname):
global data, label
self.clf = svm.SVC()
reload(fname)
self.clf.fit(data, label)
def judge(self, img):
global t_lab
try:
img = img.img
except AttributeError:
pass
return t_lab[self.clf.predict([rgbfeature(img)])[0]]
if __name__ == "__main__":
clf = SVM_clf()
pos = cpos.pos(posfile='/scene/pos.txt')
while 1:
clf.train('/pic/train_face/')
pics = []
pics = cpic.pic()
for i in range(20, 25):
pic = pics.getslice(pos.get_rio(i))
print(clf.judge(pic))
ctrl = input()
ans = ctrl.split()
for i in range(5):
pics[i].save('pic/train_face/' + ans[i] + '%s.bmp' % (str(time.time())[-6:]))