def cmd_help(self, msg):
Func.sendMessage("Available Commands:")
for m, module in Func.getBot().modules.items():
commands = m + ": "
for name, target in module.commands.items():
if name[1:2] != "@":
continue
if commands != m + ": ":
commands += ", "
if name[0:1] == "^":
name = name[1:]
if name[-1] == "$":
name = name[0:-1]
cmd = name.split(" ")
full = ""
for i in cmd:
if i[0:1] != "*" and i[0:1] != "(":
if full != "":
full += " "
full += i
commands += full
Func.sendMessage(commands)
def getneighbor(self, lgrid, rgrid):
# tmp = np.zeros(self.leftgridsize*self.neibor)
#取出self.leftgridsize*self.neibor这么一小块的部分
# rgrid=rgrid.reshape(-1)
start = self.leftgridsize * (lgrid - self.neiborwidth)
start[start <= 0] = 0
end = start + self.leftgridsize * self.neibor
if end[0] > self.img1.shape[0]:
end[0] = self.img1.shape[0]
if end[1] > self.img1.shape[1]:
end[1] = self.img1.shape[1]
mask = (self.leftimglabel[start[0]:end[0], start[1]:end[1]] >
eps).reshape(-1)
lr, lc = Func.value2index(
self.leftimglabel[start[0]:end[0], start[1]:end[1]] - 1,
shape=(self.lgn, self.lgn))
lr = np.abs(lr - lgrid[0])
lc = np.abs(lc - lgrid[1])
rr, rc = Func.value2index(
self.leftmatchgrid[start[0]:end[0], start[1]:end[1]] - 1,
shape=(self.lgn, self.lgn))
rr = np.abs(rr - rgrid[0])
rc = np.abs(rc - rgrid[1])
#取横轴坐标中大的
lr[lr <= lc] = lc[lr <= lc]
rr[rr <= rc] = rc[rr <= rc]
num = ((lr == rr) & mask).sum()
if self.DEBUG:
print(lgrid, rgrid, start, end, rr.shape, lr.shape, num)
klen = end - start
#过滤网格不匹配的
# return ((lr==rr).reshape(klen)*self.kernel[0:klen[0],0:klen[1]]).sum()
return num
def getMinMax(data, gs): # get min/max az,el for 3d plot
cordinateMin = np.max(data[0], axis=1)
cordinateMax = np.max(data[0], axis=1)
for item in data:
cordinateMaxNew = np.max(item, axis=1)
cordinateMinNew = np.min(item, axis=1)
for i in range(6):
if cordinateMaxNew[i] > cordinateMax[i]:
cordinateMax[i] = cordinateMaxNew[i]
if cordinateMinNew[i] < cordinateMin[i]:
cordinateMin[i] = cordinateMinNew[i]
xmin = min(cordinateMin[0], cordinateMin[3])
ymin = min(cordinateMin[1], cordinateMin[4])
zmin = min(cordinateMin[2], cordinateMin[5])
xmax = max(cordinateMax[0], cordinateMax[3])
ymax = max(cordinateMax[1], cordinateMax[4])
zmax = max(cordinateMax[2], cordinateMax[5])
xxrange = xmax - xmin
yrange = ymax - ymin
zrange = zmax - zmin
ab = gs.B - gs.A
abScale = [ab[0] / xxrange, ab[1] / yrange,
ab[2] / zrange] #scale axis equal
az = -90 - Func.degree(math.atan(abScale[0] / abScale[1]))
el = -np.sign(ab[1]) * Func.degree(
math.atan(abScale[2] / math.sqrt(abScale[0]**2 + abScale[1]**2)))
return [[xmin, xmax, ymin, ymax, zmin, zmax], [az, el]]
def run(self, type=0): if type == 0: self.computescoreandthre() # 计算出TrueMatcher # self.TrueMatches[np.arange(1,100,2),np.arange(1,100,2)]=1 # return self.getTrueMatch() ssds = self.drawTrueMatch() Func.imshow(ssds)
def _calCordinate(self):
A = np.array([self.A_X, self.A_Y, self.A_Z])
Ap = np.array([self.Ap_X, self.Ap_Y, self.Ap_Z])
F = np.array([self.F_X, self.F_Y, self.F_Z])
Fp = np.array([self.Fp_X, self.Fp_Y, self.Fp_Z])
F2 = np.array([self.F_X2, self.F_Y2, self.F_Z2])
Fp2 = np.array([self.Fp_X2, self.Fp_Y2, self.Fp_Z2])
B = Func.point(self.A, self.Ap, self.Distance)
E = Func.point(self.F, self.Fp, self.FE)
E2 = Func.point(self.F2, self.Fp2, self.FE2)
if self.MechanicType == 'Center':
A2 = A.copy()
Ap2 = Ap.copy()
A_X2 = self.A_X
A_Y2 = self.A_Y
A_Z2 = self.A_Z
Ap_X2 = self.Ap_X
Ap_Y2 = self.Ap_Y
Ap_Z2 = self.Ap_Z
errorP.A_X2 = self.errorP.A_X
errorP.A_Y2 = self.errorP.A_Y
errorP.A_Z2 = self.errorP.A_Z
errorP.Ap_X2 = self.errorP.Ap_X
errorP.Ap_Y2 = self.errorP.Ap_Y
errorP.Ap_Z2 = self.errorP.Ap_Z
errorN.A_X2 = self.errorN.A_X
errorN.A_Y2 = self.errorN.A_Y
errorN.A_Z2 = self.errorN.A_Z
errorN.Ap_X2 = self.errorN.Ap_X
errorN.Ap_Y2 = self.errorN.Ap_Y
errorN.Ap_Z2 = self.errorN.Ap_Z
Distance2 = (self.Distance + self.Distance2)
B2 = Func.point(A2, Ap2, Distance2)
else:
A2 = E.copy()
Ap2 = F.copy()
A_X2 = self.E_X
A_Y2 = self.E_Y
A_Z2 = self.E_Z
Ap_X2 = self.F_X
Ap_Y2 = self.F_Y
Ap_Z2 = self.F_Z
errorP.A_X2 = self.errorP.Fp_X
errorP.A_Y2 = self.errorP.Fp_Y
errorP.A_Z2 = self.errorP.Fp_Z
errorP.Ap_X2 = self.errorP.F_X
errorP.Ap_Y2 = self.errorP.F_Y
errorP.Ap_Z2 = self.errorP.F_Z
errorN.A_X2 = self.errorN.Fp_X
errorN.A_Y2 = self.errorN.Fp_Y
errorN.A_Z2 = self.errorN.Fp_Z
errorN.Ap_X2 = self.errorN.F_X
errorN.Ap_Y2 = self.errorN.F_Y
errorN.Ap_Z2 = self.errorN.F_Z
B2 = Func.point(A2, Ap2, Distance2)
def cnnnet():
learnrate = 1e-4
out = 120
# 存name。模型可取出
x_images = tf.placeholder(tf.float32, [None, 224, 224, 3], name='input_x')
# y_ = tf.placeholder(tf.float32, [None, 224, 224, 3])
y_ = tf.placeholder(tf.float32, shape=[None, out], name='input_y')
#drop out
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
#batchsize
batch_size = tf.placeholder(tf.float32, name='batch_size')
vgg = vgg16(x_images)
with tf.name_scope('softmax_layer') as scope:
# 直接在这里取最后一层全连接，使用softmax，自己定义loss函数，进行训练
sw = tf.Variable(tf.truncated_normal([4096, out],
dtype=tf.float32,
stddev=1e-1),
name='weights')
sb = tf.Variable(tf.constant(1.0, shape=[out], dtype=tf.float32),
trainable=True,
name='biases')
fc2_drop = tf.nn.dropout(vgg.fc2, keep_prob)
fc3l = tf.nn.bias_add(tf.matmul(fc2_drop, sw), sb)
# parameters += [fc3w, fc3b]
# softmax多分类器
y_conv = tf.nn.softmax(fc3l)
# 防止在0log(0)出现Nan
cross_entropy = -(
tf.reduce_sum(y_ * tf.log(tf.clip_by_value(y_conv, 1e-10, 1.0))) /
batch_size) # 定义交叉熵为loss函数,应该每次除以batch的深度，因为batch可能会不一样多
train_step = tf.train.AdamOptimizer(learnrate).minimize(
cross_entropy) # 调用优化器优化
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
#保存参数到集合，方便模型取出
tf.add_to_collection('pred_network', y_conv)
tf.add_to_collection('accuracy', accuracy)
tf.add_to_collection('loss', cross_entropy)
# sess.run(tf.global_variables_initializer()) # 变量初始化
# vgg.load_weights('../../../include_data/vgg_npz/vgg16_weights.npz', sess)
# 记录日志
with tf.name_scope('log') as scope:
#vgg.fc1
Func.variable_summaries(vgg.fc1, 'vgg_fc1')
#vgg.fc2
Func.variable_summaries(vgg.fc2, 'vgg_fc2')
# 首先再源码中加入需要跟踪的变量：
tf.summary.scalar("loss", cross_entropy) # 损失函数值
tf.summary.scalar("accuracy", accuracy) # 损失函数值
# )然后定义执行操作：
merged_summary_op = tf.summary.merge_all()
# 再session中定义保存路径：
return train_step, accuracy, cross_entropy, y_conv, vgg, x_images, y_, keep_prob, batch_size, merged_summary_op
def test(self):
self.setparam()
lens = len(self.matches)
kp1r = np.zeros([lens]).astype(np.int32)
kp1c = np.zeros([lens]).astype(np.int32)
kp2r = np.zeros([lens]).astype(np.int32)
kp2c = np.zeros([lens]).astype(np.int32)
self.leftgridsize = np.array([self.grid1h, self.grid1w])
self.rightgridsize = np.array([self.grid2h, self.grid2w])
self.leftlabel = (np.arange(1, self.rows1 ** 2 + 1).reshape(self.rows1, self.rows1))\
.repeat(self.leftgridsize[0],0)\
.repeat(self.leftgridsize[1], 1).astype(np.int32)
self.rightlabel = (np.arange(1, self.rows2 ** 2 + 1).reshape(self.rows2, self.rows2))\
.repeat(self.rightgridsize[0],0)\
.repeat(self.rightgridsize[1], 1).astype(np.int32)
grid1=np.zeros(self.rows1**2)
# 用于卷积计算阈值
leftsize = self.img1.shape[:2]
rightsize = self.img2.shape[:2]
self.leftimg = np.zeros(leftsize)
self.rightimg = np.zeros(rightsize)
for i in range(lens):
pt1 = np.array(self.kp1[self.matches[i].queryIdx].pt)
pt2 = np.array(self.kp2[self.matches[i].trainIdx].pt)
kp1r[i] = int(pt1[1])
kp1c[i] = int(pt1[0])
kp2r[i] = int(pt2[1])
kp2c[i] = int(pt2[0])
id1, id2 = self.getRegion(self.matches[i], 1)
id11=self.leftlabel[kp1r[i], kp1c[i]]-1
id22=self.rightlabel[kp2r[i], kp2c[i]]-1
if id1!=id11 or id2!=id22:
print("error:\nid1=%d but id11=%d \nid2=%d but id22=%d"%(id1,id11,id2,id22))
grid1[id1]+=1
self.leftimg[(kp1r[i],kp1c[i])] =self.leftimg[(kp1r[i],kp1c[i])]+ 1
self.rightimg[(kp2r[i], kp2c[i])] = self.rightimg[(kp2r[i], kp2c[i])] + 1
kp1list = (kp1r,kp1c)
kp2list = (kp2r,kp2c)
if (self.leftimg>1).sum()>0:
print('it is one to more!')
if(self.rightimg > 1).sum() > 0:
print('it is more to one!')
filter=np.ones(shape=self.leftgridsize)
self.leftgridkpoints= Func.conv2withstride(self.leftimg, filter, stride=self.leftgridsize, start=None, gridnum=self.rows1)
self.testimg=np.zeros(shape=(self.img1h,self.img1w))
for i in range(self.img1h):
for j in range(self.img1w):
id1 = (math.floor((j % self.img1w) / self.grid1w) + math.floor(
(i % self.img1h) / self.grid1h) * self.rows1)
self.testimg[i,j]=id1
Func.imagesc(grid1.reshape(self.rows1, self.rows1) - self.leftgridkpoints, 'GMS score')
Func.imagesc(self.testimg - self.leftlabel, 'GMS-GGF label testimg')
def gameLoop():
pygame.init()
pygame.font.init()
#pygame.mixer.init()
#pygame.mixer.music.load("audio/halo_themes.wav")
#pygame.mixer.music.play(-1)
invalids = tuple(invalids_func())
for y in range(0, screen.get_height(), 32):
for x in range(0, screen.get_width(), 32):
if Tile.total_tiles in invalids:
Tile(x, y, 'solid')
else:
Tile(x, y, 'empty')
#set time
#clock = pygame.time.Clock()
FPS = 24
total_frames = 0
background = pygame.image.load('images/Background.png')
player = Player(64, 128)
while player.health > 1:
screen.blit(background, (0, 0))
Enemy.spawn(total_frames, FPS)
Enemy.update(screen, player)
player.movement()
Bullet.super_massive_jumbo_loop(screen)
A_Star(screen, player, total_frames, FPS)
interaction(screen, player)
player.draw(screen)
Func.text_to_screen(screen, "Health {0}".format(player.health), 0, 0)
pygame.display.flip()
clock.tick(FPS)
total_frames += 1
if player.health <= 0:
sleep(2)
screen.blit(pygame.image.load("images/End.png"), (0, 0))
pygame.display.update()
break
sleep(4)
def computescoreandthre(self):
# 计算阈值
self.thre = Func.conv2withstride(self.leftimg,
self.kernel,
stride=self.stride,
start=self.start,
gridnum=self.lgn)
De = self.neibor**2
self.thre = self.TreshFactor * np.sqrt(self.thre / De) # 阈值计算公式
if self.DEBUG:
print("self.leftimg:max:%f,min:%f" %
(self.leftimg.max(), self.leftimg.min()))
print("self.thre:max:%f,min:%f" %
(self.thre.max(), self.thre.min()))
print("self.thre:max:%f,min:%f" %
(self.thre.max(), self.thre.min()))
# 计算打分
self.score = np.zeros((self.lgn, self.lgn))
self.lgshape = (self.lgn, self.lgn)
# nnb=1
for i in range(self.lgn): # r
for j in range(self.lgn): # c
if self.thre[i, j] == 0:
continue
leftvalue = Func.index2value((i, j), self.lgshape) + 1
bestmatchgrid = self.leftmatchgrid[self.leftimglabel ==
leftvalue]
if bestmatchgrid.size < 1:
continue # 点数小于阈值则不计算，默认为不匹配
number, n_counts = np.unique(bestmatchgrid, return_counts=True)
rbestindex = number[np.argsort(n_counts)[-1]]
index = (self.leftimglabel == leftvalue) & (self.leftmatchgrid
== rbestindex)
neiborsindex = (((self.leftimglabel - self.leftmatchgrid)
== (leftvalue - rbestindex))
& self.leftimg.astype(np.bool)).astype(
np.float32)
neiborsindexconv = Func.conv2withstride(neiborsindex,
self.kernel,
stride=self.stride,
start=self.start,
gridnum=self.lgn)
self.score[i, j] = neiborsindexconv[i, j]
if self.DEBUG:
print(
"calc grid(%d,%d)\n thre=%f,index.sum=%d,neiborsindex.sum=%d,score=%f"
% (i, j, self.thre[i, j], index.sum(),
neiborsindex.sum(), self.score[i, j]))
if neiborsindexconv[i, j] < self.thre[i, j]:
continue
# nnb += 1
self.TrueMatches += index
def FilDelta(n_clicks, value1, lid, children):
if (n_clicks != None) & (value1 != None) & (lid != None) & (children !=
None):
optionsInfo_C = pd.DataFrame(
json.loads(json.loads(children)['optionsInfo_C']))
optionsInfo_P = pd.DataFrame(
json.loads(json.loads(children)['optionsInfo_P']))
optionsInfo = pd.DataFrame(
json.loads(json.loads(children)['optionsInfo']))
optionsInfo = optionsInfo.sort_values('STRIKE_PRC')
optionsInfo_C = optionsInfo_C.sort_values('STRIKE_PRC')
optionsInfo_P = optionsInfo_P.sort_values('STRIKE_PRC')
optionsInfo.index = range(len(optionsInfo))
optionsInfo_C.index = range(len(optionsInfo_C))
optionsInfo_P.index = range(len(optionsInfo_P))
# lldate=list(set(optionsInfo.EXPIR_DATE.values))
# lldate.sort()
lid.sort()
lldate = lid
Selec_C = Func.DeltaStreikFilter(optionsInfo_C, lldate, value1)
Selec_P = Func.DeltaStreikFilter(optionsInfo_P, lldate, value1)
callRIC = pd.DataFrame(Selec_C).T
callRIC.columns = lldate
call = pd.DataFrame([
optionsInfo[optionsInfo.Instrument.isin(
Selec_C[i])].STRIKE_PRC.values for i in range(len(Selec_C))
]).T
call.columns = lldate
putRIC = pd.DataFrame(Selec_P).T
putRIC.columns = lldate
put = pd.DataFrame([
optionsInfo[optionsInfo.Instrument.isin(
Selec_P[i])].STRIKE_PRC.values for i in range(len(Selec_P))
]).T
put.columns = lldate
RE = {
"callRIC": callRIC.to_json(),
"call": call.to_json(),
"putRIC": putRIC.to_json(),
"put": put.to_json(),
}
return (json.dumps(RE))
else:
return (None)
def draw_tiles(screen):
half = Tile.width // 2
for tile in Tile.List:
if not (tile.type == 'empty'):
pygame.draw.rect(screen, [40, 40, 40], tile)
if tile.G != 0:
Func.text_to_screen(screen,
tile.G,
tile.x,
tile.y + half,
colour=[120, 157, 40])
if tile.H != 0:
Func.text_to_screen(screen,
tile.H,
tile.x + half,
tile.y + half,
colour=[20, 69, 150])
if tile.F != 0:
Func.text_to_screen(screen,
tile.F,
tile.x + half,
tile.y,
colour=[56, 177, 177])
Func.text_to_screen(screen, tile.number, tile.x, tile.y)
def run(interval, command):
Log.info("*" * 51)
#log.print_ts("Command %s"%command)
Log.info("Run every %s seconds." % interval)
Log.info("*" * 51)
main = Func.Func()
monitor = Monitor.Monitor()
while True:
try:
# sleep for the remaining seconds of interval
time_remaining = interval - time.time() % interval
Log.info("Sleeping until %s(%.3f seconds).." % (time.strftime(
"%Y-%m-%d %H:%M:%S",
time.localtime(
(time.time() + time_remaining))), time_remaining))
time.sleep(time_remaining)
Log.info("-" * 51)
#log.print_ts("Starting command.")
Log.info("Starting Run...")
# execute the command
main.Run()
monitor.Run()
Log.info("Stop...")
#status = os.system(command)
Log.info("-" * 51)
#log.print_ts("Command status = %s."%status)
except Exception as e:
Log.error("LogtextArchived.run() error code: %s" % (str(e)))
def controls():
progress = 0
controlsImage = pygame.image.load("Images/ControlsPg.png")
progressOutlineImage = pygame.image.load("Images/progressBar.png")
t = 15
screen.blit(controlsImage, (0, 0))
loadingBlockX = 111
screen.blit(progressOutlineImage, (108, 10))
while t:
pygame.draw.rect(screen, white, (loadingBlockX, 14, 55, 15))
Func.text_to_screen(screen, "Loading: ", 8, 8)
pygame.display.flip()
loadingBlockX += 48.25
clock.tick(1)
t -= 1
def computescoreandthre(self):
# 计算阈值
filter = np.ones(self.leftgridsize)
print("self.leftimg:max:%f,min:%f" % (self.leftimg.max(), self.leftimg.min()))
self.leftgridkpoints = Func.conv2withstride(self.leftimg, filter, stride=self.leftgridsize, start=None, gridnum=self.lgn)
print("self.leftgridkpoints:max:%f,min:%f" % (self.leftgridkpoints.max(), self.leftgridkpoints.min()))
# 显示计数
# Func.imagesc(tmp, '左图计数')
threfilter = np.ones((3, 3)) / 9 # 计算均值
# threfilter = np.ones((3, 3))
self.thre = ss.convolve2d(self.leftgridkpoints, threfilter,'same')
print("self.thre:max:%f,min:%f" % (self.thre.max(), self.thre.min()))
self.thre = self.TreshFactor * np.sqrt(self.thre*(self.leftgridkpoints>0)) # 阈值计算公式
print("self.thre:max:%f,min:%f"%(self.thre.max(),self.thre.min()))
# 显示阈值
# Func.imagesc(self.thre, 'thre')
# 计算打分
self.score = np.zeros((self.lgn, self.lgn))
self.lgshape=(self.lgn,self.lgn)
neiborwidth = 1 # 多远 的算邻居
filtershape=(self.leftgridsize[0]*(2*neiborwidth+1),self.leftgridsize[1]*(2*neiborwidth+1))
neiborfilter = np.ones(filtershape)
nnd=1
for i in range(self.lgn): # r
for j in range(self.lgn): # c
if self.leftgridkpoints[i,j]==0:
continue
leftvalue=Func.index2value((i,j),self.lgshape)+1
bestmatchgrid=self.leftmatchgrid[self.leftimglabel==leftvalue]
if bestmatchgrid.size <1:
continue # 点数小于阈值则不计算，默认为不匹配
number,n_counts=np.unique(bestmatchgrid,return_counts=True)
rbestindex=number[np.argsort(n_counts)[-1]]
index=(self.leftimglabel==leftvalue)&(self.leftmatchgrid==rbestindex)
neiborsindex=(((self.leftimglabel-self.leftmatchgrid)==(leftvalue-rbestindex))&(self.leftimg>0)).astype(np.int32)
lsize=np.array(self.leftgridsize)
neiborsindexconv = Func.conv2withstride(neiborsindex,neiborfilter,
stride=self.leftgridsize,start=None,gridnum=self.lgn)
self.score[i, j]=neiborsindexconv[i,j]
# print("calc grid(%d,%d)\n thre=%.f,index.sum=%d,neiborsindex.sum=%d,score=%d"
# % (i, j,self.thre[i,j],index.sum(),neiborsindex.sum(),self.score[i, j]))
nnd+=1
if neiborsindexconv[i,j]<self.thre[i,j]:
continue
self.TrueMatches+= index
print("batchsize %d"%(nnd))
def InsData(self, url, newskind, DateDif):
res = Func.GetHeader(url)
if res == '':
print 'Time Out!'
else:
soup = bs(res, 'html.parser')
souptitle = soup.find('h2', attrs={'class': 'news_title'})
title = souptitle.text
timesoup = soup.find('span', attrs={'class': 'gray9 mgl10'})
timestr = timesoup.text
newstime = datetime.datetime.strptime(timestr, '%Y-%m-%d %H:%M')
source = ''
cmt = 0
author = ''
txtsoup = soup.find('div', attrs={'class': 'newsDetail_txt'})
newstxt = txtsoup.text.strip()
now = datetime.datetime.now() - datetime.timedelta(days=DateDif)
currdate = newstime
if (now.year == currdate.year and now.month == currdate.month
and now.day == currdate.day) or DateDif == -1:
conn = MySQLdb.connect(host='.',
port=3306,
user='******',
passwd='123456',
db='news_info',
charset='utf8')
cur = conn.cursor()
#insert数据
dictypename = {1: '双色球', 2: '福彩3D', 3: "七彩乐"}
sql = 'insert into tbl_news_info(title,newstype,source,pubtime,typename,webname,newsurl,contenttxt,joincount,author) select \'%s\',\'%s\',\'%s\',\'%s\',\'%s\',\'%s\',\'%s\',\'%s\',%d,\'%s\' from DUAL' % (
title, '其它', source, newstime, dictypename[newskind],
'澳客网', url, newstxt, cmt, author)
try:
cur.execute(sql)
cur.close()
conn.commit()
conn.close()
except Exception, e:
print 'str(Exception):\t', str(Exception)
print 'str(e):\t\t', str(e)
print 'repr(e):\t', repr(e)
print 'e.message:\t', e.message
print 'traceback.print_exc():'
traceback.print_exc()
print 'traceback.format_exc():\n%s' % traceback.format_exc(
)
print 'url:%s' % url
print '########################################################'
finally:
pass
def cmd_alpha(self, msg, alpha):
res = Var.client.query(alpha)
try:
interpretation = res.pods[0].text
link = "http://wolframalpha.com/input/?i=" + urllib.quote_plus(
alpha)
Func.sendMessage(interpretation + " ('" + alpha + "') " + link)
first = True
for i in res.pods:
if first:
first = False
continue
Func.sendMessage(i.text)
except Exception:
return "No results found on WolframAlpha for '" + alpha + "'"
def createlabel(self, shift=(0, 0), show=False):
# 生成标签矩阵
self.leftlabel = (np.arange(1, self.lgn ** 2 + 1).reshape(self.lgn, self.lgn)) \
.repeat(self.leftgridsize[0], 0).repeat(self.leftgridsize[1], 1).astype(np.int32)
self.rightlabel = (np.arange(1, self.rgn ** 2 + 1).reshape(self.rgn, self.rgn)) \
.repeat(self.rightgridsize[0], 0).repeat(self.rightgridsize[1], 1).astype(np.int32)
if shift[0] == 1:
self.leftlabel = np.roll(self.leftlabel,
-int(self.leftgridsize[0] / 2),
axis=0)
self.rightlabel = np.roll(self.rightlabel,
-int(self.rightgridsize[0] / 2),
axis=0)
if shift[1] == 1:
self.leftlabel = np.roll(self.leftlabel,
-int(self.leftgridsize[1] / 2),
axis=1)
self.rightlabel = np.roll(self.rightlabel,
-int(self.rightgridsize[1] / 2),
axis=1)
# 标签
self.leftimglabel[self.kp1list] = self.leftlabel[self.kp1list]
# 只保存匹配特征所在的网格，反正也不会计算其实际坐标
self.leftmatchgrid[self.kp1list] = self.rightlabel[
self.kp2list] # index2value(rightkpt, shape)
if self.DEBUG or show:
Func.imagesc(self.leftlabel,
"leftlabel shift=(%d,%d)" % (shift[0], shift[1]))
Func.imagesc(self.leftimglabel,
"leftimglabel shift=(%d,%d)" % (shift[0], shift[1]))
Func.imagesc(self.leftmatchgrid,
"leftmatchgrid shift=(%d,%d)" % (shift[0], shift[1]))
def cmd_a(self, msg, alpha):
if not (msg.FromHandle in Var.lastalpha):
Var.lastalpha[msg.FromHandle] = 0
if Var.lastalpha[msg.FromHandle] > time.time() - 60:
return "Please wait for " + str(
round(60 - (time.time() - Var.lastalpha[msg.FromHandle]),
2)) + " seconds!"
Var.lastalpha[msg.FromHandle] = time.time()
res = Var.client.query(alpha)
try:
interpretation = res.pods[0].text
result = res.pods[1].text
link = "http://wolframalpha.com/input/?i=" + urllib.quote_plus(
alpha)
Func.sendMessage(interpretation + " ('" + alpha + "') " + link)
return result
except Exception:
return "No results found on WolframAlpha for '" + alpha + "'"
def main():
print(__name__)
root='./images/'
# img1path='./images/000.png'
# img2path = './images/020.png'
img1path=root+'img1.jpg'
img2path = root+'img2.jpg'
# img1path='./images/img.jpg'
# img2path = './images/img2.jpg'
img1=cv2.imread(img1path)
img2=cv2.imread(img2path)
ddsize=(640,480)
img1 = cv2.resize(img1, ddsize)
img2 = cv2.resize(img2, ddsize)
#gms
time_start = time.time()
gms = GMS(img1, img2)
gms.run()
gms.show()
time_end = time.time(); # time.time()为1970.1.1到当前时间的毫秒数
print('cost time is %fs' % (time_end - time_start))
# #GMSwithMatrix
# time_start=time.time()
# gmsm=GMSwithMatrix(img1,img2)
# gmsm.run()
# time_end=time.time();#time.time()为1970.1.1到当前时间的毫秒数
# print('cost time is %fs'%(time_end-time_start))
#GMSwithGridFilter
time_start = time.time()
ggf = GMSwithGridFilter(img1, img2)
ggf.run()
time_end = time.time(); # time.time()为1970.1.1到当前时间的毫秒数
print('cost time is %fs' % (time_end - time_start))
print('var %f' % (gms.listgrid1.reshape(gms.rows1, gms.rows1) - ggf.leftgridkpoints).var())
# Func.imagesc(ggf.leftgridkpoints, 'GGF point')
#测试
# TreshFactor=6
# leftgridkpoints=gms.listgrid1.reshape(gms.rows1, gms.rows1)
# threfilter = np.ones((3, 3))
# thre = cv2.filter2D(leftgridkpoints, -1, threfilter)
# delta=ggf.thre-gms.thre.reshape(gms.rows1,gms.rows1)
# print("gms.thre:max:%f,min:%f" % (gms.thre.max(), gms.thre.min()))
# print("thre sum=%f var=%f,mean=%f"%(delta.sum(),delta.var(),delta.mean()))
# Func.imagesc(gms.thre.reshape(gms.rows1, gms.rows1), 'GMS thre')
# Func.imagesc(ggf.thre, 'GGF thre')
# Func.imagesc(delta, 'GGF thre - GMS thre')
delta=ggf.score-gms.score.reshape(gms.rows1,gms.rows1)
print("gms.score:max:%f,min:%f" % (gms.score.max(), gms.thre.min()))
print("ggf.score:max:%f,min:%f" % (ggf.score.max(), ggf.thre.min()))
print("score sum=%f var=%f,mean=%f" % (delta.sum(), delta.var(), delta.mean()))
Func.imagesc(gms.score.reshape(gms.rows1, gms.rows1), 'GMS score')
Func.imagesc(ggf.score, 'GGF score')
Func.imagesc(delta, 'GGF score - GMS score')
#gms.show()
a=np.ones([4,4])
b=np.ones([3,3])
def MessageStatus(self, msg, status):
print(msg.FromHandle + ": " + msg.Body)
if Var.chat.qsize() >= 100:
Var.chat.get()
pass
Var.chat.put("[" + Func.now() + "] " + msg.FromHandle + ": " +
msg.Body + "\n")
for m, module in self.modules.items():
for regexp, target in module.commands.items():
match = re.match(regexp, msg.Body, re.IGNORECASE)
if match:
msg.MarkAsSeen()
#try:s
reply = module.runMethod(target, msg, match)
if reply:
Func.sendMessage(reply)
#except Exception as ex:
# e = traceback.format_stack()
# e[-1] += "\n"
# e.append(ex[0])
# code, error = Func.pastebin(e)
# Func.sendMessage("Error: " + error)
return
if len(msg.Body) > 0:
if msg.Body[0:1] == "@":
Func.sendMessage("Command Not Found")
def MessageStatus(self, msg, status):
print(msg.FromHandle + ": " + msg.Body)
if Var.chat.qsize() >= 100:
Var.chat.get()
pass
Var.chat.put("[" + Func.now() + "] " + msg.FromHandle + ": " + msg.Body + "\n")
for m, module in self.modules.items():
for regexp, target in module.commands.items():
match = re.match(regexp, msg.Body, re.IGNORECASE)
if match:
msg.MarkAsSeen()
#try:s
reply = module.runMethod(target, msg, match)
if reply:
Func.sendMessage(reply)
#except Exception as ex:
# e = traceback.format_stack()
# e[-1] += "\n"
# e.append(ex[0])
# code, error = Func.pastebin(e)
# Func.sendMessage("Error: " + error)
return
if len(msg.Body) > 0:
if msg.Body[0:1] == "@":
Func.sendMessage("Command Not Found")
def defineNumbers(orig):
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
custom_config = r'--oem 3 --psm 6 outputbase digits'
v = []
for i in range(len(orig)):
aux = []
for j in range(len(orig[i])):
img = Func.cleanSquare(orig[i][j])
cv2.imshow('Janela ' + str(i) + ' ' + str(j), img)
s = pytesseract.image_to_string(img, config=custom_config)
aux.append(retireNoise(s))
v.append(aux)
return (v)
def classify(self, img):
IHandler = Func.PornImageHandler()
#img = cv2.imread(filename)
num = 1
VecList = []
label = []
# 将图片特征向量
VecList.append(IHandler.GetFeature(img))
label.append(1)
num += 1
if os.path.exists(self.modelpath):
model = svm.svm_load_model(self.modelpath)
else:
self.train()
p_labs, p_acc, p_vals = svm.svm_predict(label, VecList, model, '-b 1')
print p_labs, p_acc, p_vals
if p_labs[0] > 0.5:
return True
else:
return False
def ExtractFeature(self, truepath, falsepath):
num = 1
VecList = []
label = []
self.truelist = self.__load_images(truepath)
self.falselist = self.__load_images(falsepath)
IHandler = Func.PornImageHandler()
# 训练正样本
for img in self.truelist:
self.__SubProc(IHandler, VecList, label, img, 1, num,
len(self.truelist) + len(self.falselist))
num += 1
# 训练负样本
for img in self.falselist:
self.__SubProc(IHandler, VecList, label, img, 0, num,
len(self.truelist) + len(self.falselist))
num += 1
print "[*] Created Process Pool!"
print len(VecList), " ", len(label)
return label, VecList
def cmd_capture(self, msg):
if Var.lastcapture > time.time() - 60:
return "Please wait for " + str(
round(60 - (time.time() - Var.lastcapture), 2)) + " seconds!"
lastcapture = time.time()
newChat = Queue.Queue()
oldChat = []
while not Var.chat.empty():
message = Var.chat.get()
newChat.put(message)
oldChat.append(message)
code, paste = Func.pastebin(oldChat)
chat = newChat
if code == 1:
return paste
else:
return "Capture: " + paste
def loadtemplate(self):
self.wb = xl.load_workbook(self.template_path)
patch_worksheet()
self.sheet1 = self.wb['Input']
list1 = [[5, 2, 5], [5, 9, 12], [5, 15, 18], [12, 5, 8], [12, 15, 18],
[14, 7, 8], [14, 15, 18], [16, 6, 7], [16, 10, 11],
[16, 17, 18], [22, 2, 5], [22, 11, 14], [24, 4, 5],
[24, 9, 10], [24, 13, 14], [26, 4, 5], [26, 9, 10],
[26, 13, 14], [26, 18, 19], [29, 2, 4], [29, 9, 11],
[29, 15, 17], [32, 2, 4], [32, 9, 11], [32, 15,
17], [38, 2, 4],
[38, 9, 11], [38, 15, 17], [41, 2, 4], [41, 9, 11],
[41, 15, 17], [44, 2, 4], [44, 9, 11], [44, 15,
17], [47, 2, 4],
[47, 9, 11], [47, 15, 17], [49, 4, 5], [49, 9, 10],
[49, 17, 19], [55, 2, 4], [55, 9, 11], [55, 15,
17], [58, 2, 4],
[58, 9, 11], [58, 15, 17], [61, 2, 4], [61, 9, 11],
[61, 15, 17], [64, 2, 4], [64, 9, 11], [64, 15,
17], [66, 4, 5],
[66, 9, 10], [66, 17, 18], [19, 14, 20], [35, 14, 20],
[21, 7, 8], [22, 7, 8], [21, 16, 17], [22, 16, 17]]
list4 = [[5, 2, 8], [5, 9, 15]]
list5 = [[14, 2, 8], [15, 3, 8], [16, 3, 8], [31, 2, 8], [32, 3, 8],
[33, 3, 8]]
for lists in [list1, list4, list5]:
for item in lists:
x = item[0]
item.insert(2, x)
list1.extend([[70, 2, 71, 5], [70, 7, 71, 9], [70, 11, 71, 13],
[70, 15, 71, 20]])
self.sheet2 = self.wb['Optimization']
self.sheet3 = self.wb['Input']
self.sheet4 = self.wb['Output-Alphanumeric']
self.sheet5 = self.wb['Output-Alphanumeric (2)']
self.sheet6 = self.wb['Output-Tolerance calcuation']
for item1 in list1:
Func.writeborder(self.sheet1, item1)
for item4 in list4:
Func.writeborder(self.sheet4, item4)
for item5 in list5:
Func.writeborder(self.sheet5, item5)
def run(P, truepath, falsepath):
IHandler = Func.PornImageHandler()
truelist = __load_images(truepath)
falselist = __load_images(falsepath)
# Multi Proc
VecList = []
label = []
result = []
num = 1
pool = Pool()
for img in truelist:
result.append(pool.apply_async(SubProc, args = (IHandler, img, 1, num, len(truelist) + len(falselist), )))
num += 1
for img in falselist:
result.append(pool.apply_async(SubProc, args = (IHandler, img, 0, num, len(truelist) + len(falselist), )))
num += 1
pool.close()
pool.join()
for r in result:
print r.get()[1]
VecList.append(r.get()[0])
label.append(r.get()[1])
return label, VecList
def FrameMasking(image, Face1_lower, Face1_upper, Face2_lower, Face2_upper,
Face3_lower, Face3_upper):
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
filtered = cv2.bilateralFilter(hsv, 5, 10, 10)
sharpened = unsharped_filter(filtered)
mask_Face1 = cv2.inRange(sharpened, Face1_lower, Face1_upper)
mask_Face2 = cv2.inRange(sharpened, Face2_lower, Face2_upper)
mask_Face3 = cv2.inRange(sharpened, Face3_lower, Face3_upper)
mask_Face1 = Func.erode(mask_Face1, cv2.MORPH_ELLIPSE, (3, 3))
mask_Face1 = Func.dilate(mask_Face1, cv2.MORPH_ELLIPSE, (3, 3))
mask_Face2 = Func.erode(mask_Face2, cv2.MORPH_ELLIPSE, (3, 3))
mask_Face2 = Func.dilate(mask_Face2, cv2.MORPH_ELLIPSE, (3, 3))
mask_Face3 = Func.erode(mask_Face3, cv2.MORPH_ELLIPSE, (3, 3))
mask_Face3 = Func.dilate(mask_Face3, cv2.MORPH_ELLIPSE, (3, 3))
#mask_face1 = cv2.bitwise_not(mask_Face1)
#frame = mask_Face2 + mask_Face1
frame = cv2.bitwise_and(mask_Face3, mask_Face2, mask=mask_Face1)
return mask_Face1, mask_Face2, mask_Face3, frame
url = urllib.request.urlopen(Oper + key)
tree = ET.parse(url)
note=tree.getroot()
#정리해서 보여줌
indent(note)
#xml 문서 그대로 보여줌
#ET.dump(note)
if Operation=="일일":
Func.daily(note)
elif Operation=="주간":
Func.weekly(note)
elif Operation=="영화목록":
Func.MovieList(note)
elif Operation=="영화사목록":
Func.CompanyList(note)
answer=input("메일을 보내시겠습니까? Y/N ")
if answer=="Y"or answer== "y":
m=gmail.Mail()
m.login()
m.write()
m.add(Func.tag_list,Func.value_list)
m.send()
# self.close()
# def closeEvent(self, ce):
# self.fileQuit()
def about(self):
QMessageBox.about(
self, "About", """embedding_in_qt5.py example
Copyright 2015 BoxControL
This program is a simple example of a Qt5 application embedding matplotlib
canvases. It is base on example from matplolib documentation, and initially was
developed from Florent Rougon and Darren Dale.
http://matplotlib.org/examples/user_interfaces/embedding_in_qt4.html
It may be used and modified with no restriction; raw copies as well as
modified versions may be distributed without limitation.
""")
if __name__ == '__main__':
app = QApplication(sys.argv)
outputs = parameters.Outputs()
gs = parameters.GS()
outjson = 'output.json'
Func.LoadJson(outjson, outputs, gs)
aw = ApplicationWindow(gs)
aw.show()
#sys.exit(qApp.exec_())
app.exec_()
def __init__(self):
self.skype = Skype4Py.Skype(Events=self)
self.skype.Attach()
self.loadComamnds()
Func.sendMessage("/me " + Var.version + " Initialized")
def on_getIPBtn_clicked(self):
domainName = self.domainInput.text()
ipaddress = Func.getIP(domainName)
self.ipInput.setText(ipaddress)
def traceInfo(info):
sExceptMsg = ""
if Config.B_SYS_DEBUG:
sExceptMsg += "\r\n**********************Trace %s*********************************\r\n"%(Func.fNow())
sExceptMsg += "\r\nTrace Messages:%s"%(str(info))
sExceptMsg += "\r\n"
if Config.B_SYS_WRITE_LOG:
logger = LogInfo.initlog()
logger.info(info)
if Config.B_SYS_TRACE:
print "\r\nTrace stack is flow:\r\n"
traceback.print_stack()
print "\r\n"
if Config.B_SYS_DEBUG:
sExceptMsg += "\r\n*********************Trace END**********************************\r\n"
print sExceptMsg
def writeLog(logLevel,logMessage):
# alogLevel = ["trace","warning","error"]
sExceptMsg = ""
if Config.B_SYS_DEBUG:
sExceptMsg += "\r\n**********************%s %s*********************************\r\n"%(logLevel.upper(),Func.fNow())
sExceptMsg += "\r\n%s Messages:%s"%(logLevel.upper(),str(logMessage))
sExceptMsg += "\r\n"
if logLevel=="trace":
if Config.B_SYS_WRITE_LOG:
logger = LogInfo.initlog()
logger.info(logMessage)
if Config.B_SYS_TRACE:
print "\r\nTrace stack is flow:\r\n"
traceback.print_stack()
print "\r\n"
elif logLevel=="warning":
pass
elif logLevel=="error":
exc_type, exc_value, exc_traceback = sys.exc_info()
if exc_type!=None:
sExceptMsg += "\r\n"
sExceptMsg += repr(traceback.format_tb(exc_traceback))
sExceptMsg += "\r\n"
else:
print "\r\nTrace stack is flow:\r\n"
traceback.print_stack()
sExceptMsg += "\r\n"
if Config.B_SYS_WRITE_LOG and exc_type!=None:
logger = LogInfo.initlog()
logger.error(sExceptMsg)
if Config.B_SYS_DEBUG:
sExceptMsg += "\r\n*********************%s END**********************************\r\n"%(logLevel.upper())
print sExceptMsg
elif lineNum==mainBlock[0]:
func = True
elif lineNum==mainBlock[1]:
func = False
else:
if line[len(line)-1]=='\n':
line = line[:len(line)-1]
if quote:
pass
elif switch:
if line.lstrip()=="{": pass
elif line.lstrip()=="}":
switch = False
Func.Switch().delVarName()
else:
newLines = Func.generate(Func.determineBegin(line),line)
if newLines!= None:
for newLine in newLines:
if lineNum>mainBlock[0] and lineNum<mainBlock[1]: mainCode.append(tab*'\t' + newLine + '\n')
else: newProg.append(tab*'\t' + newLine + '\n')
elif func:
if line.lstrip()=="}":
tab -= 1
if line=="\t}": func = False
if Loop:
if lineNum>mainBlock[0] and lineNum<mainBlock[1]: mainCode.append((tab+Func.add())*'\t' + Func.getToAppend() + '\n')
else: newProg.append((tab+Func.add())*'\t' + Func.getToAppend() + '\n')
Loop = False
Func.add()
elif line.lstrip()=="{":
tab += 1
def SubProc(Func, img, label, num, length):
print "%3.2f %% \t (%4d/%4d)"%(float(num) * 100 / length, num, length)
return Func.GetFeature(img), label
def __init__(self):
self.skype = Skype4Py.Skype(Events=self)
self.skype.Attach()
self.loadComamnds()
Func.sendMessage("/me " + Var.version + " Initialized")
