def main():
args = sys.argv[1:]
if (args[0] == "log"):
log()
elif (args[0] == "show"):
if (len(args) == 1):
show()
def command_line(dictionary):
while True:
command = input('Enter your command: ')
if command == 'exit':
return 'exit'
elif command == 'move':
functions.move()
elif command == 'help':
for command, description in list(commands_list.items()):
print(command + ' = ' + description)
elif command == 'add':
functions.add()
return functions.update()
elif command == 'remove':
functions.remove()
elif command == 'print':
continue
elif command == 'show':
functions.show()
elif command == 'train':
return functions.train(dictionary)
#analiza factoriala
#elimin rotatia
model_fa = fact.FactorAnalyzer(rotation=None)
#construiesc modelul - standardizeaza modelul
model_fa.fit(x)
#calcul scoruri
f = model_fa.transform(x)
#plot al scorurilor
functions.plot_scoruri(
f[:,
0], f[:,
1], list(t.index), "F1", "F2", "Plot Scoruri - Analiza Factoriala"
) #coloana 1 din scoruri, aferenta primei componente principale
#matrice de corelatii
l = model_fa.loadings_
#varianta factorilor
alpha_fa = model_fa.get_factor_variance() #aici merge facuta si tabelare
# model factorial sklearn - daca e fara factor_analyzer -- alta metoda de factorizare folosita
model_fa_sk = dec.FactorAnalysis(n_components=3)
model_fa_sk.fit(x)
#extragem scorurile
f_sk = model_fa_sk.transform(x)
functions.plot_scoruri(
f_sk[:, 0], f_sk[:, 1], list(t.index), "F1", "F2",
"Plot Scoruri SK - Analiza Factoriala"
) #coloana 1 din scoruri, aferenta primei componente principale
functions.show()
def show(a):
functions.show(window, a)
from functions import create, remove, menu, show
menu()
n = int(input("Uildliin dugaar oruulna uu: "))
if n == 1:
show()
elif n == 2:
create()
elif n == 3:
remove()
elif n == 4:
print("homework")
else:
print("Ta programmaas garlaa")
mode = sys.argv[2]#leitura do modo
pathImg = sys.argv[1]#caminho da imagem
#pega a mensagem oculta no arquivo messagem
file_message = open("messagem.txt", "r")
message = str()
for line in file_message:
message += line
file_message.close()
if mode == 'ocultar':
img = Image.open(pathImg)
length = img.size
if(verifyLength(message, img)):
hide(message, img, length)
else:
print("Imagem com tamanho insufiente!")
elif mode == "mostrar":
img = Image.open(pathImg)
text = show(img)
print(text)
file_message_show = open("messagem_revelada.txt", 'w')
for line in text:
file_message_show.write(line)
file_message_show.close()
print("4 - Update pay")
print("5 - Exit")
option = input()
if option == "1":
while True:
print("---VIEWING MODE---")
print("1 - See full list of employees")
print("2 - See full list of managers")
print("3 - See full list of developers")
print("4 - See full list of interns")
print("5 - Go back to main menu")
viewOption = input()
if viewOption == "1":
print("Showing list of all employees...\n")
show("SELECT * FROM employees")
elif viewOption == "2":
print("Showing list of all managers...\n")
show("SELECT * FROM employees WHERE position = 'manager'")
elif viewOption == "3":
print("Showing list of all developers...\n")
show("SELECT * FROM employees WHERE position = 'developer'")
elif viewOption == "4":
print("Showing list of all interns...\n")
show("SELECT * FROM employees WHERE position = 'intern'")
elif viewOption == "5":
print("Returning to main menu...\n")
break
else:
print("Invalid option.\n")
if (im[i][j] >= 210):
im[i][j] = 255
else:
im[i][j] = 0
im = ~im
imgFinal = np.copy(im)
(rows, cols) = im.shape
estructura = np.array([[1], [1], [1]])
#Falta por convertir a manual
imgFinal = cv2.erode(imgFinal, estructura, iterations=1)
imgFinal = cv2.dilate(imgFinal, estructura, iterations=1)
imgFinal = cv2.bitwise_not(imgFinal)
return imgFinal
"""
test = f.loadimg("test.png")
f.show(test)
test = f.to_grey(test)
test = cv2.adaptiveThreshold(test,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,15,-2)
f.saveimg(test)
data = line_separator(test)
result = separator_blocks(test,data)
#mg(test)
"""
#Demo1
#Demo
def get_body(image):
gray = cv2.medianBlur(image, 5)
focus, show, contrast, checkBrightness)
# Import Image and resize
image = cv2.imread('flower.jpg')
image = resize(image)
imageOriginal = image.copy()
# Checks the brightness of the image and adjusts
#If the image is too blurry, improve contrast by equalizing histogram channels
image = equalize(image)
# Find the item you want to enlarge. If it is not correct, adjust the k value to an odd number from 0-9.
k = 5
contours = contours(image, k)
rect, drawing = findPage(contours, imageOriginal)
name = 'Box Drawn'
show(drawing, name)
# Warp the boxed item to full screen. You can play with focusing/sharpening the image based on your needs.
warp = transform(imageOriginal, rect)
warp = checkBrightness(warp)
# Focus/Sharpen the scanned photo. Change the alpha value to adjust the level of focus in the photo
alpha = 4
warp = focus(warp, alpha)
warp = sharp(warp)
name = 'Scanned Photo'
show(warp, name)
