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Deteksi Tomat.py
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Deteksi Tomat.py
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from Tkinter import *
import tkMessageBox as msgbox
import tkFileDialog
import cv2
import math
import numpy as np
import scipy as scp
from PIL import Image, ImageTk
import matplotlib
from matplotlib import pyplot as plt
from matplotlib.figure import Figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
matplotlib.use('TkAgg')
class Main:
def __init__(self,parent,title):
self.parent = parent
self.parent.title(title)
self.parent.config(background="#316068")
global kernel_h
global kernel_w
kernel_h = 3
kernel_w = 3
self.komponen()
#==================================================================== GUI =========================================================================================
def komponen(self):
self.frameFoto = Frame(self.parent,bg="#316068")
self.frameFoto.grid(row=0,column=0,sticky=NW)
self.histFoto = Frame(self.parent,bg="#316068")
self.histFoto.grid(row=0,column=1,sticky=NW)
self.imgDef = Image.open("kosong.png")
self.imgDef = ImageTk.PhotoImage(self.imgDef)
label1 = Label(self.frameFoto,width=25,height=2,fg="black",text="Ambil Gambar Tomat")
label1.grid(row=0,column=0,sticky=N,columnspan=2)
label2 = Label(self.histFoto,width=25,height=2,fg="black",text="Histogram")
label2.grid(row=0,column=0,sticky=N)
self.fotoInput = Label(self.frameFoto,image=self.imgDef,width=256,height=256)
self.fotoInput.grid(row=1,column=0,padx=10,pady=10,sticky=N,columnspan=2)
self.fotoInput.image = self.imgDef
self.fotoZoom = Label(self.frameFoto,image=self.imgDef,width=256,height=256)
self.fotoZoom.grid(row=2,column=0,sticky=N,padx=10,pady=10, columnspan=2)
self.fotoZoom.image = self.imgDef
self.btnBrowse = Button(self.frameFoto, text='Load Photo',command=self.ambilImg,width=20,height=2,bg="#a1dbcd")
self.btnBrowse.grid(row=3,column=0,sticky=N,pady=10,padx=2.5)
self.btnXtract = Button(self.frameFoto, text='Feature Extraction',command=self.mulai,width=20,height=2,bg="#a1dbcd")
self.btnXtract.grid(row=3,column=1,sticky=N,pady=10,padx=2.5)
self.btnGO = Button(self.frameFoto, text='Identifikasi',command=self.identify,width=45,height=2,bg="green")
self.btnGO.grid(row=4,column=0,sticky=N,pady=10,padx=10,columnspan=2)
labelR = Label(self.histFoto, fg="red",text="Histogram Merah")
labelR.grid(row=1,column=0,sticky=N, pady=10, padx=50)
self.histMerah = Figure(figsize=(4,1.7), dpi=100)
canvas = FigureCanvasTkAgg(self.histMerah, master=self.histFoto)
canvas.get_tk_widget().grid(row=2,column=0,sticky=N)
labelG = Label(self.histFoto,fg="green",text="Histogram Hijau")
labelG.grid(row=3,column=0,sticky=N, pady=10, padx=50)
self.histHijau = Figure(figsize=(4,1.7), dpi=100)
canvas = FigureCanvasTkAgg(self.histHijau, master=self.histFoto)
canvas.get_tk_widget().grid(row=4,column=0,sticky=N)
labelB = Label(self.histFoto,fg="blue",text="Histogram Biru")
labelB.grid(row=5,column=0,sticky=N, pady=10, padx=50)
self.histBiru = Figure(figsize=(4,1.7), dpi=100)
canvas = FigureCanvasTkAgg(self.histBiru, master=self.histFoto)
canvas.get_tk_widget().grid(row=6,column=0,sticky=N)
self.nilai = Frame(self.parent, bg="#FFFFFF")
self.nilai.grid(row=0, column=2, sticky=NW, padx=30)
labelNilaiRGB = Label(self.nilai, fg="black", text="Nilai RGB")
labelNilaiRGB.grid(row=0, column=0, sticky=N, pady=10, columnspan=2)
labelMaxRGB = Label(self.nilai, fg="black", text="MAX RGB")
labelMaxRGB.grid(row=1, column=0, sticky=N, pady=10, columnspan=2)
x = StringVar()
self.NilaiMax = Entry(self.nilai,bd=4,width=20, textvariable=x)
self.NilaiMax.grid(row=2,column=0,sticky=N,padx=10,pady=10, columnspan=2)
x.set(str(self.maxRGB()))
labelMean = Label(self.nilai, fg="black", text="MEAN")
labelMean.grid(row=3, column=0, sticky=N, pady=5, columnspan=2)
labelMeanR = Label(self.nilai, fg="red", text="RED")
labelMeanR.grid(row=4, column=0, sticky=N, pady=5, columnspan=2)
self.mR = StringVar()
self.meanRed = Entry(self.nilai,bd=4,width=20, textvariable=self.mR)
self.meanRed.grid(row=5,column=0,sticky=N, columnspan=2)
self.mR.set("0")
labelMeanG = Label(self.nilai, fg="green", text="GREEN")
labelMeanG.grid(row=6, column=0, sticky=N, pady=5, columnspan=2)
self.mG = StringVar()
self.meanGreen = Entry(self.nilai,bd=4,width=20, textvariable=self.mG)
self.meanGreen.grid(row=7,column=0,sticky=N, columnspan=2)
self.mG.set("0")
labelMeanB = Label(self.nilai, fg="blue", text="BLUE")
labelMeanB.grid(row=8, column=0, sticky=N, pady=5, columnspan=2)
self.mB = StringVar()
self.meanBlue = Entry(self.nilai,bd=4,width=20, textvariable=self.mB)
self.meanBlue.grid(row=9,column=0,sticky=N, columnspan=2)
self.mB.set("")
labelNorm = Label(self.nilai, fg="black", text="NORMALISASI")
labelNorm.grid(row=10, column=0, sticky=N, pady=10, columnspan=2)
self.N1 = StringVar()
self.Norm1 = Entry(self.nilai,bd=4,width=20, textvariable=self.N1)
self.Norm1.grid(row=11,column=0,sticky=N, columnspan=2)
self.N1.set("")
self.N2 = StringVar()
self.Norm2 = Entry(self.nilai,bd=4,width=20, textvariable=self.N2)
self.Norm2.grid(row=12,column=0,sticky=N, pady=5, columnspan=2)
self.N2.set("")
self.N3 = StringVar()
self.Norm3 = Entry(self.nilai,bd=4,width=20, textvariable=self.N3)
self.Norm3.grid(row=13,column=0,sticky=N, columnspan=2)
self.N3.set("")
labelHasil = Label(self.nilai, fg="black", text="HASIL PROSES ADALINE")
labelHasil.grid(row=14, column=0, sticky=N, pady=10, columnspan=2)
labelNet = Label(self.nilai, fg="black",text="NET")
labelNet.grid(row=15, column=0, sticky=N, pady=10)
self.Net = StringVar()
self.valueNet = Entry(self.nilai,bd=3,width=20, textvariable=self.Net)
self.valueNet.grid(row=15,column=1,sticky=NW, pady=10, padx=4)
self.Net.set("")
labelNet = Label(self.nilai, fg="black",text="KESIMPULAN")
labelNet.grid(row=16, column=0, sticky=NW, pady=10)
self.result = StringVar()
self.conclusion = Entry(self.nilai,bd=3,width=20, textvariable=self.result)
self.conclusion.grid(row=16,column=1,sticky=N, pady=10,padx=4)
self.result.set("")
self.Learning = Frame(self.parent,bg="#FFFFFF")
self.Learning.grid(row=0,column=3,sticky=NW, padx=30, pady=10)
label30 = Label(self.Learning, fg="black", text="LEARNING")
label30.grid(row=0, column=0, sticky=N, columnspan=2)
labelTarget = Label(self.Learning, fg="black", text="TARGET")
labelTarget.grid(row=1, column=0, sticky=N, pady=10)
self.target = StringVar()
self.setTarget = Entry(self.Learning,fg="blue", bd=4, width=10, textvariable=self.target)
self.setTarget.grid(row=1,column=1, sticky=N, pady=10,padx=5)
self.target.set("unset")
self.saveBtn = Button(self.Learning, text='Save to Dataset',command=self.simpandata,width=20,height=1,bg="#a1dbcd")
self.saveBtn.grid(row=2,column=0,sticky=N,pady=10,padx=10, columnspan=2)
self.learnBtn = Button(self.Learning, text='Learn', command=self.adaline, width=20,height=1, bg="green")
self.learnBtn.grid(row=3, column=0,sticky=N, pady=10, padx=10, columnspan=2)
self.spaceFrame = Frame(self.Learning, bg="#FFFFFF")
self.spaceFrame.grid(row=4, column=0, sticky=N, pady=50, columnspan=2)
self.resetBtn = Button(self.Learning, text='Reset Bobot', command=self.resetWeightAndBias, width=20,height=1, bg="red", fg="black")
self.resetBtn.grid(row=5, column=0, sticky=N, pady=10, padx=10, columnspan=2)
self.destroyBtn = Button(self.Learning, text='Hapus Dataset', command=self.clearDataset, width=20,height=1, bg="red", fg="black")
self.destroyBtn.grid(row=6, column=0, sticky=N, pady=10, padx=10, columnspan=2)
#==================================================================== FUNGSI =========================================================================================
def maxRGB(self):
n = 256*256*255*3
return n
def tampilImg(self):
self.fotoOutput.config(image=self.image_show,width=320,height=320)
self.fotoOutput.image = self.image_show
def ambilImg(self):
self.path = tkFileDialog.askopenfilename()
if(len(self.path) > 0):
image = cv2.imread(self.path)
h = image.shape[0]
w = image.shape[1]
for i in range(h):
for j in range(w):
px_b = float(image[i,j,0])
px_g = float(image[i,j,1])
px_r = float(image[i,j,2])
image[i,j,0] = px_r
image[i,j,1] = px_g
image[i,j,2] = px_b
resized_image = cv2.resize(image, (256,256))
self.image_show = Image.fromarray(resized_image)
self.image_show = ImageTk.PhotoImage(self.image_show)
self.fotoInput.config(image=self.image_show,width=256,height=256)
self.fotoZoom.config(image=self.image_show,width=256,height=256)
def mulai(self):
image = cv2.imread(self.path)
resized_image = cv2.resize(image, (256,256))
rows = resized_image.shape[0]
cols = resized_image.shape[1]
r=0
g=0
b=0
for i in range(rows):
for j in range(cols):
red = int(image[i,j,2])
r = r+red
green = int(image[i,j,1])
g = g+green
blue = int(image[i,j,0])
b = b+blue
meanR = float(r)/self.maxRGB()
self.mR.set(meanR)
meanG = float(g)/self.maxRGB()
self.mG.set(meanG)
meanB = float(b)/self.maxRGB()
self.mB.set(meanB)
self.normalisasi()
def normalisasi(self):
d = []
d.append(float(self.mR.get()))
d.append(float(self.mG.get()))
d.append(float(self.mB.get()))
new_min = 0
new_max = 1
v = []
for i in range(3):
x = (d[i]-min(d))*(new_max - new_min)/(max(d) - min(d))
x = x+new_min
v.append(x)
self.N1.set(v[0])
self.N2.set(v[1])
self.N3.set(v[2])
def simpandata(self):
if(self.target.get() != 'unset'):
x = []
file = open("dataset.txt","r")
for item in file.read().split():
x.append(float(item))
file.close()
x.append(float(self.N1.get()))
x.append(float(self.N2.get()))
x.append(float(self.N3.get()))
x.append(float(self.target.get()))
file = open("dataset.txt","w")
endl = 0
for item in x:
file.write("%s " % item)
endl += 1
if(endl==4):
file.write("\n")
endl=0
file.close()
print x
def perceptron(self):
x = []
x.append(float(self.N1.get()))
x.append(float(self.N2.get()))
x.append(float(self.N3.get()))
lr = 0.5
target = [1,0,-1]
f = open("bobot.txt","r")
w = f.readlines()
f.close()
def adaline(self):
lr = 0.1
d = 1
f = open("dataset.txt","r")
data = f.read().split()
f.close()
epoh = 1
while d>0.05:
print "epoh : ",epoh
for j in range(len(data)/4):
x = []
i=j*4
while i<j*4+3:
x.append(float(data[i]))
i+=1
t = float(data[j*4+3])
print "x : ",x
f = open("bobot.txt","r")
w = f.read().split()
f.close()
net = 0
for k in range(3):
net += x[k]*float(w[k])
f = open("bias.txt","r")
b = float(f.read())
f.close()
net += b
print "net = ",net
y = net
print "y = ",y
print "t-y = ",t-y
dw = [0,0,0]
for k in range(3):
dw[k] = lr*x[k]*(t-y)
print dw
db = lr*(t-y)
for k in range(3):
w[k] = str(float(w[k])+dw[k])
print "w : ",w
f = open("bobot.txt","w")
for item in w:
f.write("%s " % item)
f.close()
f = open("bias.txt","w")
f.write("%s" % str(b+db))
f.close()
d = max(dw)
print ""
epoh += 1
def identify(self):
self.mulai()
x = []
x.append(float(self.N1.get()))
x.append(float(self.N2.get()))
x.append(float(self.N3.get()))
file = open("bobot.txt","r")
w = file.read().split()
file.close()
file = open("bias.txt","r")
b = float(file.read())
file.close()
net = 0
for i in range(3):
net += x[i]*float(w[i])
net += b
self.Net.set(str(net))
#fungsi aktivasi
if net<1.5:
r = 1
self.result.set("MATANG")
elif net<2.4:
r = 2
self.result.set("KURANG MATANG")
else:
r = 3
self.result.set("MASIH MENTAH")
def resetWeightAndBias(self):
file = open("password.txt","r")
p = file.read()
file.close()
c = raw_input("ketik password : ")
if c == p:
print "password benar"
'''
file = open("bobot.txt","w")
for i in range(3):
file.write("0 ")
file.close()
file = open("bias.txt","w")
file.write("0")
file.close()
'''
else:
print "password salah, operasi dibatalkan"
def clearDataset(self):
file = open("password.txt","r")
p = file.read()
file.close()
c = raw_input("ketik password : ")
if c == p:
print "password benar"
'''
file = open("bobot.txt","w")
for i in range(3):
file.write("0 ")
file.close()
file = open("bias.txt","w")
file.write("0")
file.close()
'''
else:
print "password salah, operasi dibatalkan"
#==================================================================== MAIN =========================================================================================
root = Tk()
Main(root,".:: APLIKASI PENDETEKSI KEMATANGAN TOMAT ::.")
root.mainloop()