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Main.py
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Main.py
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# -*- coding: utf-8 -*-
from ui import mainUI as mu
from PyQt5.QtWidgets import QWidget, QPushButton, QFileDialog, QDialog
from PyQt5.QtCore import Qt
from PyQt5.QtGui import QPixmap, QImage
#from GLCM import GLCM
#from Preprocessing import DataFrame, Dataset, ImageFolder
#from Learning import kNN, GNB
#import cv2 as cv
import sys
import os
sys.path.append(os.getcwd() + '\\')
print(os.getcwd())
class Main(QWidget):
__folderUrl = ""
__csvUrl = ""
__csvName = "Dataset.csv"
__imgUrl = ""
def __init__(self):
super().__init__()
self.ui = mu.Ui_Form()
self.ui.setupUi(self)
self.ui.selectFolder_radio.toggled.connect(self.radioFolderSelected)
self.ui.selectCSV_radio.toggled.connect(self.radioCSVSelected)
self.ui.selectFolder_edit.setDisabled(True)
self.ui.selectCSV_edit.setDisabled(True)
self.ui.selectFolder_browser.setDisabled(True)
self.ui.selectCSV_browser.setDisabled(True)
self.ui.selectImage_button.setDisabled(True)
self.ui.selectFolder_browser.clicked.connect(self.folderBrowserClicked)
self.ui.selectCSV_browser.clicked.connect(self.csvBrowserClicked)
self.ui.startTrain_button.clicked.connect(self.startTrainClicked)
self.ui.colorspace_combo.currentTextChanged.connect(self.colorspaceChanged)
self.ui.selectImage_button.clicked.connect(self.selectImageClicked)
self.show()
def radioFolderSelected(self, enable):
if enable:
self.ui.selectFolder_edit.setDisabled(False)
self.ui.selectFolder_browser.setDisabled(False)
self.ui.selectCSV_edit.setDisabled(True)
self.ui.selectCSV_browser.setDisabled(True)
def radioCSVSelected(self, enable):
if enable:
self.ui.selectFolder_edit.setDisabled(True)
self.ui.selectFolder_browser.setDisabled(True)
self.ui.selectCSV_edit.setDisabled(False)
self.ui.selectCSV_browser.setDisabled(False)
def colorspaceChanged(self):
self.ui.colorspace_label.setText(str(self.ui.colorspace_combo.currentText()))
def selectImageClicked(self):
url = str(QFileDialog.getOpenFileName(self, "Select File")[0])
if url != "":
self.__imgUrl = url
self.setInputImage(self.__imgUrl)
print(self.__imgUrl)
def setInputImage(self, path):
self.__inputImg = cv.imread(path)
self.__inputPixmap = QPixmap(path)
self.ui.inputImage_label.setPixmap(self.__inputPixmap)
self.__inputImgName = path.split('/')[-1]
self.classified()
def classified(self):
if (self.ui.colorspace_combo.currentText() == "Grayscale"):
self.__inputImg = cv.cvtColor(self.__inputImg, cv.COLOR_BGR2GRAY)
elif (self.ui.colorspace_combo.currentText() == "HSV"):
self.__inputImg = cv.cvtColor(self.__inputImg, cv.COLOR_BGR2HSV)
else:
print()
self.getGLCM()
ori = str(self.learningModel.predict(self.__iX)[0])
self.ui.originalClass_label.setText(ori)
self.ui.predictedClass_label.setText(str(self.__inputImgName.split('.')[2]))
print("\n", self.__inputImgName.split('.'))
def getGLCM(self):
g = GLCM.GLCM(self.__inputImg)
self.__glcm = g.getGLCM()
self.__sumX, self.__sumY = g.getSumGLCM(self.__glcm)
self.__meanX, self.__meanY = g.getMean(self.__glcm, self.__sumX, self.__sumY)
self.__varX, self.__varY = g.getVarianceXY(self.__glcm, self.__sumX, self.__sumY, self.__meanX, self.__meanY)
self.__sdX, self.__sdY = g.getStandardDeviation(self.__varX, self.__varY)
self.__mean = [self.__meanX, self.__meanY]
self.__asm = g.getASM(self.__glcm)
self.__contrast = g.getContrast(self.__glcm)
self.__correlation = g.getCorrelation(self.__glcm, self.__meanX, self.__meanY, self.__sdX, self.__sdY)
self.__variance = g.getVariance(self.__glcm, self.__meanX, self.__meanY)
self.__idm = g.getIDM(self.__glcm)
self.__entropy = g.getEntropy(self.__glcm)
self.__variables = {"img": self.__inputImgName, "race": self.__inputImgName.split('.')[0], "mean": self.__mean, "asm": self.__asm, "contrast": self.__contrast, "correlation": self.__correlation, "variance": self.__variance, "idm": self.__idm, "entropy": self.__entropy, "class": self.__inputImgName.split('.')[2]}
self.__columns = ["img", "race", "mean", "asm", "contrast", "correlation", "variance", "idm", "entropy", "class"]
dfObj = DataFrame.DataFrame(self.__variables, self.__columns, os.getcwd() + '\\' + "xx.csv")
dfInputImg = dfObj.getDataFrame()
dfObj.saveDataFrame(dfInputImg)
DS = Dataset.Dataset(os.getcwd() + '\\' + "xx.csv")
self.__iX, self.__iY = DS.getXY()
#print(self.__iX, self.__iY)
def folderBrowserClicked(self):
url = str(QFileDialog.getExistingDirectory(self, "Select Directory"))
if url != "":
self.__folderUrl = url + '/'
self.ui.selectFolder_edit.setText(self.__folderUrl)
print(self.__folderUrl)
def csvBrowserClicked(self):
url = str(QFileDialog.getOpenFileName(self, "Select File")[0])
if url != "":
self.__csvUrl = url
self.ui.selectCSV_edit.setText(self.__csvUrl)
print(self.__csvUrl)
def startTrainClicked(self):
self.learningSelection = str(self.ui.learning_combo.currentText())
self.colorspace = str(self.ui.colorspace_combo.currentText())
if (self.ui.selectFolder_radio.isChecked()):
self.__folderUrl = str(self.ui.selectFolder_edit.text())
IF = ImageFolder.ImageFolder(self.__folderUrl, self.colorspace)
IF.featureExtract(os.getcwd() + '\\' + self.__csvName)
DS = Dataset.Dataset(os.getcwd() + '\\' + self.__csvName)
self.df = DS.getDF()
self.X, self.Y = DS.getXY()
else:
self.__csvUrl = str(self.ui.selectCSV_edit.text())
DS = Dataset.Dataset(self.__csvUrl)
self.df = DS.getDF()
self.X, self.Y = DS.getXY()
print(self.X, self.Y)
if (self.learningSelection == "k-NN"):
k = int(self.ui.selectK_edit.text())
self.learning = kNN.kNN(k, self.X, self.Y)
self.learningModel = self.learning.getModel()
self.accuracy = self.learning.getAccuracy()
else:
self.learning = GNB.GNB(self.X, self.Y)
self.learningModel = self.learning.getModel()
self.accuracy = self.learning.getAccuracy()
print(self.accuracy)
self.ui.accuracy_label.setText("%.2f" % (self.accuracy * 100))
self.ui.selectImage_button.setDisabled(False)
mainWindow = Main()