def load_image(photo_name):
root= Tk()
root.geometry("500x400+300+300")
root.wm_iconbitmap('img1.ico')
f=os.listdir("/karishma/project")
root.title('Scanned QR Code')
photo=PhotoImage(file=photo_name)
label=Label(root,image=photo,width=600,height=350)
label.pack(anchor=N)
r=read(photo_name)
label_1=Label(root,text="Employee details :{}".format(r),font=("System", 3), justify = 'left')
label_1.pack(anchor=S)
root.mainloop()
def Controller():
mode = initialization()
userID = 0
userName = ""
userSession = -1
while 1:
if mode == "NORMAL":
mode = normal()
elif mode == "AUTH":
userList = auth(
) # auth() returns a list of the form [ID, Name, Session - True/False, Mode - Read/Set]
userID = userList[0]
userName = userList[1]
userSession = userList[2]
mode = userList[3]
elif mode == "READ":
readList = read(
userID, userSession
) # read(userID, userSession) returns a list of the form [Session, Mode]
userSession = readList[0]
if userSession == False:
userID = 0
userName = ""
mode = readList[1]
else:
mode = "READ"
elif mode == "SET":
setList = set(
userID, userSession
) # set(userID, userSession) returns a list of the form [Session, Mode]
userSession = setList[0]
if userSession == False:
userID = 0
userName = ""
mode = setList[1]
else:
mode = "SET"
elif mode == "TEACHER":
mode = teacher()
elif mode == "ADMIN":
mode = admin()
def train(self):
"""
Training: gather all required items for finding probabilities.
"""
docList = {}
allwords = []
for folder in self.classes:
print "Reading files in:", folder
docList[folder], words = read(folder, self.basePath)
allwords += words
self.allWords = list(set(allwords))
self.vocab = len(self.allWords)
self.docList = docList
print "Data Loaded."
def Controller: mode = initialization() userID = 0 userName = "" userSession while 1: if mode == "NORMAL": mode = normal() elif mode == "AUTH": userList = auth() # auth() returns a list of the form [ID, Name, Session - True/False, Mode - Read/Set] userID = userList[0] userName = userList[1] userSession = userList[2] mode = userList[3] elif mode == "READ": readList = read(userID, userSession)# read(userID, userSession) returns a list of the form [Session, Mode] userSession = readList[0] if userSession == False: userID = 0 userName = "" mode = readList[1] else: mode = "READ" elif mode == "SET": setList = set(userID, userSession) # set(userID, userSession) returns a list of the form [Session, Mode] userSession = setList[0] if userSession == False: userID = 0 userName = "" mode = setList[1] else: mode = "SET" elif mode == "TEACHER": mode = teacher() elif mode == "ADMIN": mode = admin()
listCost.append(arrayCost)
arrayGradient = (np.dot(np.transpose(X), arrayLoss) /
X.shape[0]) + (λ * ω)
arrayGradientSum += arrayGradient**2
arraySigma = np.sqrt(arrayGradientSum)
ω -= η * arrayGradient / arraySigma
if itera % 1000 == 0:
print("iteration:{}, cost:{} ".format(itera, arrayCost))
return ω, listCost
## 讀入資料
arrayTrainX, arrayTrainY, mean_x, std_x = read(TRAIN_PATH, True)
## Training
# Adagrad
intLearningRate = 1.27
arrayW = np.zeros((arrayTrainX.shape[1], 1)) # (163, )
arrayW_ada, listCost_ada = Adagrad(X=arrayTrainX,
Y=arrayTrainY,
ω=arrayW,
η=intLearningRate,
Iteration=20000,
λ=0)
## Save weight
np.save('../Weight/weight_best.npy', arrayW_ada)
np.save('../Weight/mean_x_best.npy', mean_x)
def init():
"""Map Initializtion"""
global time, config, pedes, num_out, generatorSet, count, statictics
global Length, Width, length, width, row_start, col_start
pedes = []
num_out = 0
count = 0
del pedes[:]
statictics = []
time = 0
config = read("map")
for i in range(0, length):
for j in range(0, width):
Map[i][j].set(config[i, j])
generatorSet = []
# North
for i in range(125, 135):
g = Generator(121, i, len(generatorSet))
generatorSet.append(g)
for i in range(85, 100):
g = Generator(121, i, len(generatorSet))
generatorSet.append(g)
# East
for i in range(110, 122):
g = Generator(i, 134, len(generatorSet))
generatorSet.append(g)
for i in range(75, 85):
g = Generator(i, 134, len(generatorSet))
generatorSet.append(g)
for i in range(35, 45):
g = Generator(i, 134, len(generatorSet))
generatorSet.append(g)
for i in range(15, 25):
g = Generator(i, 134, len(generatorSet))
generatorSet.append(g)
# South
for i in range(125, 135):
g = Generator(15, i, len(generatorSet))
generatorSet.append(g)
for i in range(105, 115):
g = Generator(15, i, len(generatorSet))
generatorSet.append(g)
# West
for i in range(90, 98):
g = Generator(i, 80, len(generatorSet))
generatorSet.append(g)
for i in range(35, 40):
g = Generator(i, 80, len(generatorSet))
generatorSet.append(g)
for i in range(0, length):
for j in range(0, width):
config[i, j] = Map[i][j].getstate()
Length, Width = config.shape
if row_start > Length:
row_start = 0
if col_start > Width:
col_start = 0
if row_start + length > Length:
length = Length - row_start
if col_start + width > Width:
width = Width - col_start
#This code will take number of particles and number of states, and a hamiltoninan to create the ham matrix from numpy import * from math import * from sympy import * from optparse import OptionParser from read import * mat = read() quant = [2,2] mat.quantum(quant) #n = mat.size() #v = mat.ham() #eig = mat.energy() #teste = mat.clusterenergy(v) #tau0 = mat.tau0() #tau0 = zeros((2,2)) #tau0[0,0] = 4.0 #tau0[1,0] = 6.0 #tau0[1,1] = 2.0 #print tau0 #tau = mat.newtau(tau0)
"""
Prepare the data
"""
from read import *
"""
read has two methods: read and show
"""
"""
Reads the training data
"""
training_iterator = read(dataset='training', path='../data')
"""
We use feature extraction first
We will compute the following elements of an image
y - the class
avg_pixel - the mean of pixel, a measurement of brightness
num_black - number of non-white pixels
"""
data = np.empty([0, 3])
for instance in training_iterator:
y = instance[0]
X = instance[1]
X_pos = X > 0
avg_pixel = X.sum() / X.size
num_black = (X > 0).sum()
sym_pixel = (np.fliplr(X > 0) & X > 0)
data = np.concatenate((data, np.matrix([y, avg_pixel, num_black])), axis = 0)
read_flag = 1
play("stop.mp3", 24000)
if hand is 8:
read_flag = 0
if hand is 2:
translate('now.txt', 'en')
txt2mp3('trans_en', 'en')
play('trans_en.mp3', 24000)
#read
if read_flag is 0:
read_flag = read(read_flag, orig)
# update the FPS counter
fps.update()
# show the output frame
cv2.imshow("Text Detection", orig)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# stop the timer and display FPS information
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
## 引入讀資料檔
from read import *
### 需要改成讀參數
TEST_PATH = sys.argv[1]
OUTPUT_PATH = sys.argv[2]
WEIGHT_PATH = (sys.argv[3]==0) and './Weight/weight.npy' or './Weight/weight_best.npy'
MEAN_PATH = (sys.argv[3]==0) and './Weight/mean_x.npy' or './Weight/mean_x_best.npy'
STD_PATH = (sys.argv[3]==0) and './Weight/std_x.npy' or './Weight/std_x_best.npy'
mean_x = np.load(MEAN_PATH)
std_x = np.load(STD_PATH)
## Load data
arrayTestX = read(TEST_PATH, False, mean_x, std_x)
## Load Weight
w = np.load(WEIGHT_PATH)
ans_y = np.dot(arrayTestX, w)
ans_y = np.round(ans_y)
## Store result
with open(OUTPUT_PATH, mode='w', newline='') as submit_file:
csv_writer = csv.writer(submit_file)
header = ['id', 'value']
print(header)
csv_writer.writerow(header)
for i in range(240):
row = ['id_' + str(i), ans_y[i][0]]
csv_writer.writerow(row)
from ped_update import *
from Generator import *
from write import *
from math import *
import scipy as SP
import matplotlib
matplotlib.use('TkAgg')
import pylab as PL
from path import *
import pycxsimulator
############################################
state = read("map")
static = []
for i in range(1, 9):
name_open = "static" + str(i)
stat = read(name_open)
static.append(stat)
##########################################
Map = build_map(state, static)
"""total = 45
while (total < 100):
total = total + 1
global time, config, pedes,num_out,generatorSet,count, statictics
global Length, Width, length, width, row_start, col_start
data = read("map")
config = data
def ReadNotePad(self):
read(self.filename)
def assertExpressionRead(inputString, result): inputStream = Stream(inputString) expression = read(inputStream) assert(expressionToString(expression) == result)
