def MainFunction(kind, Remimg):
os.system("clear")
if kind == 0:
img = FigureInput()
try:
Remimg = img.copy()
except:
return
try:
if img == -1:
return
except:
pass
InpStr = input("Lock the figure?[Y/ n] ")
if InpStr == "N" or InpStr == "n":
kind = 0
else:
kind = 1
else:
img = Remimg.copy()
os.system("clear")
print("-1) Another Figure")
print("1) Gray level linear transformation.")
print("2) Gamma(power) transformation.")
print("3) Contrast-Stretching Transformations.")
print("4) Histogram Equalization")
print("5) Maximum entropy for thresholding")
print("6) Get Gradient of figure")
print("7) Thresholding with Gradient")
print("8) Statistic the figure")
print("9) Auto algorithm!")
print("10) Monte Carlo Average Constrast")
print("11) Convolution")
print("12) Histogram Equalization with Monte Carlo")
print("13) Gradient transformation Algorithm (GTA!)")
print("14) 2 Side Gradient Treasholding with DFS")
print("15) Median Filtering")
print("16) Sobel 3 * 3 cross edge detection operator")
print("17) Roberts cross edge detection operator")
print("18) Gradient Treasholding with Convolution")
print("19) The smallest variance smoothing filter")
print("20) Canny edge detector")
print("21) Random Walk Algorithm")
print("22) Unsharp masking")
print("23) Block Algorithm")
print("24) Toboggan Algorithm")
print("25) Linear PDE method")
print("26) Non-linear PDE method")
print("27) Bilateral Filter Smoothing")
print("28) Least square estimation")
print("29) Iterative solution")
print("30) Fourier Transform")
print("31) Histogram Fourier Transform")
print("32) Entropy Analysis")
print("33) Wavelet Algorithm")
print("34) Gauss Random")
print("35) ADMM Algorithm")
print("0) EXIT")
InpInt = 0
while 1:
InpStr = input("Input the algorithm you need: ")
try:
InpInt = int(InpStr)
except ValueError:
print("Input Error")
continue
else:
if InpInt < -1 or InpInt > 50:
print("Input Error")
continue
else:
break
if InpInt == -1:
MainFunction(0, [])
return
if InpInt == 0:
return
if InpInt == 1:
img = Proj1.GLLT(img)
if InpInt == 2:
img = Proj1.GPT(img)
if InpInt == 3:
img = Proj1.CST(img)
if InpInt == 4:
img = Proj1.HE(img)
if InpInt == 5:
MET(img)
MainFunction(kind, Remimg)
return
if InpInt == 6:
GradFig.GetGradient(img)
MainFunction(kind, Remimg)
return
if InpInt == 7:
img = GradFig.GT(img)
if InpInt == 8:
GradFig.FigSta(img, 0)
MainFunction(kind, Remimg)
return
if InpInt == 9:
img = Algorithm.Algorithm(img)
if InpInt == 10:
img = Algorithm.MCAC(img)
if InpInt == 11:
img = Convolution.Convolution(img)
Output(img)
return
if InpInt == 12:
img = Algorithm.MCHE(img)
if InpInt == 13:
img = GradFig.GTA(img)
if InpInt == 14:
img = GradFig.TGT(img)
if InpInt == 15:
img = Proj2.MF(img)
if InpInt == 16:
img = Proj2.SO(img)
if InpInt == 17:
img = Proj2.RO(img)
if InpInt == 18:
img = Proj2.GOC(img)
if InpInt == 19:
img = Proj2.SVSF(img)
if InpInt == 20:
img = Algorithm.CED(img)
if InpInt == 21:
img = Algorithm.RWPI(img)
if InpInt == 22:
img = Proj3.UM(img)
if InpInt == 23:
img = Proj3.BlockAlg(img)
if InpInt == 24:
img = Algorithm.TobAlgo(img)
if InpInt == 25:
img = Proj4.PDEMethod(img, 1)
if InpInt == 26:
img = Proj4.PDEMethod(img, 2)
if InpInt == 27:
img = cv2.bilateralFilter(img, 9, 35, 35)
if InpInt == 28:
img = Proj5.LSE(img)
if InpInt == 29:
img = Proj5.IS(img)
if InpInt == 30:
img = Trans.FT(img)
if InpInt == 31:
Trans.HFT(img)
MainFunction(kind, Remimg)
return
if InpInt == 32:
img = ImgEnp.Main(img)
if InpInt == 33:
img = ProjTest.Wavelet(img)
if InpInt == 34:
img = ProjTest.Gauss(img)
if InpInt == 35:
img = ProjTest.ADMM(img)
Output(img)
InpStr = input("Use the new figure?[Y/n] ")
if InpStr == "Y" or InpStr == "y":
Remimg = img.copy()
MainFunction(kind, Remimg)
return
