/
runGui.py
executable file
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/
runGui.py
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#!/usr/bin/env python
'''
Created on Nov 12, 2012
@author: ego
'''
import sys
from GUI.gui import Ui_MainWindow
from PyQt4 import QtGui, QtCore
from data.injectData import injectData
from data.processData import processData
from data.inputData import inputData
import Lamstar
import os
import numpy as np
class runGUI(QtGui.QMainWindow):
def __init__(self, parent=None):
super(runGUI, self).__init__(parent)
self.ui = Ui_MainWindow()
self.ui.setupUi(self)
self.createConnections()
self.trainingDir = 'data/images/ORL/ORL/'
self.trainPat = self.trainingDir + 'ORL.pat'
self.testingDir = 'data/images/ORL/ORL/unseen/'
self.testPat = self.testingDir + 'unseen.pat'
self.debug = 1
def createConnections(self):
self.ui.actionLoad_Training_Folder.triggered.connect(self.loadDataFiles)
self.ui.actionLoad_Testing_Folder.triggered.connect(self.loadDataFiles)
self.ui.actionTrain.triggered.connect(self.train)
self.ui.actionRun_Test.triggered.connect(self.train)
self.ui.actionPrepare_for_training.triggered.connect(self.prepareTraining)
self.ui.actionPrepare_for_testing.triggered.connect(self.prepareTesting)
def loadDataFiles(self):
if self.sender() == self.ui.actionLoad_Training_Folder:
Call = 'Training'
else:
Call = 'Testing'
dialog = QtGui.QFileDialog()
dialog.setDirectory('.')
dialog.setFileMode(QtGui.QFileDialog.Directory)
dialog.exec_()
filenames = dialog.selectedFiles()
if Call == 'Training':
self.trainingDir = str(filenames[0])
self.trainPat = self.trainingDir + '/' + self.trainingDir.rpartition('/')[-1] + '.pat'
else:
self.testingDir = str(filenames[0])
self.testPat = self.testingDir + '/' + self.testingDir.rpartition('/')[-1] + '.pat'
def prepareTraining(self):
if self.trainingDir is None:
return None
self.log('Cleaning previous temporary data ...', 'main')
# self.cleanDir(self.trainingDir)
self.log('Starting training operations ...', 'main')
directory = self.trainingDir
outdir = directory + '/'
self.log('Getting faces ...', 'query')
# fd = faceDetector(outdir,outdir)
# fd.run()
self.log('OK', 'status')
self.log('Saving patterns in the .pat file ...', 'query')
injectData(self.trainPat, outdir, 'train')
self.log('OK', 'status')
def prepareTesting(self):
if self.testingDir is None:
return None
self.log('Cleaning previous temporary data ...', 'main')
# self.cleanDir(self.testingDir)
self.log('Starting testing operations ...', 'main')
directory = self.testingDir
outdir = directory + '/'
self.log('Getting faces ...', 'query')
# fd = faceDetector(outdir,outdir)
# fd.run()
self.log('OK', 'status')
self.log('Saving patterns in the .pat file ...', 'query')
injectData(self.testPat, outdir, 'test')
self.log('OK', 'status')
def train(self):
allSamples = goodSamples = 0.0
self.log('Starting the real training...', 'main')
if self.sender() == self.ui.actionTrain:
procData = processData(self.trainPat)
result = procData.readContents()
data = inputData()
data.addAll(result)
self.ls = Lamstar.lamstar(15, 1)
for iter in range(10):
print('Iteration %s' % iter)
for i in range(data.getCount()):
# print('Training data : ', i)
# self.arr2Image(data.getWholeArray(i),'input')
# raw_input('Enter')
self.ls.train(data.getSubWords(i), data.getOutputs(i))
if(self.debug > 0):
self.log('Iteration no:' + str(iter), 'main')
self.log('No of nodes' + str(self.ls.getNoOfNodes()), 'main')
elif self.sender() == self.ui.actionRun_Test:
goodSamples = allSamples = 0
procData = processData(self.testPat)
result = procData.readContents()
data = inputData()
data.addAll(result)
files = []
for afile in os.listdir(self.testingDir):
if afile[-3:] == 'jpg' and afile.find('procced') != -1:
files.append(afile)
files.sort()
print('Quering for test data')
self.log('Quering for test data', 'main')
print('number of items: %s' % (data.getCount()))
for i in range(data.getCount()):
self.log('Querying for ' + files[i].rpartition('/')[-1], 'query')
print('Querying for ' + files[i].rpartition('/')[-1])
self.arr2Image(data.getWholeArray(i), 'input')
(result, BMU) = self.ls.query(data.getSubWords(i))
arr = self.constructArr(BMU)
self.arr2Image(arr, 'output')
self.log(result, 'status')
allSamples += 1
if files[i].find(result + '-') != -1:
goodSamples += 1
self.ui.resultLabel.setText(result)
self.log('No of nodes: ' + str(self.ls.getNoOfNodes()), 'main')
self.log('No of links: ' + str(self.ls.getNoOfLinks()), 'main')
self.log('Accuracy = ' + str((float(goodSamples) / allSamples) * 100) + '%', 'main')
print ('Good sample=%s all=%s' % (goodSamples, allSamples))
def constructArr(self, BMU):
dx = 50
dy = 40
arr = np.zeros((200, 150))
BMU.sort()
for (i, idx) in BMU:
x = (i / 5) * 50
y = (i % 5) * 40
arr[y:y + dy, x:x + dx] = self.ls.getInputNode(i, idx) * 255
return arr
def arr2Image(self, arr, holder):
from scipy.misc import imsave
imsave('tmp.png', arr)
# imshow(arr)
if holder == 'input':
self.ui.imageInputLabel.setPixmap(QtGui.QPixmap('tmp.png'))
elif holder == 'output':
self.ui.imageResultLabel.setPixmap(QtGui.QPixmap('tmp.png'))
# self.ui.imageInputLabel.setPixmap
self.ui.imageInputLabel.repaint()
def cleanDir(self, inputDir):
for afile in os.listdir(inputDir):
if afile.find('procced') != -1:
os.remove(inputDir + '/' + afile)
def log(self, text, mode):
if(mode == 'query'):
self.ui.textBrowser.setAlignment(QtCore.Qt.AlignRight)
self.ui.textBrowser.setTextColor(QtCore.Qt.black)
self.ui.textBrowser.insertPlainText(text)
elif(mode == 'status'):
self.ui.textBrowser.insertPlainText('\t[ ')
self.ui.textBrowser.setTextColor(QtCore.Qt.green)
self.ui.textBrowser.insertPlainText(text)
self.ui.textBrowser.setTextColor(QtCore.Qt.black)
self.ui.textBrowser.insertPlainText(' ]\n')
elif(mode == 'main'):
self.ui.textBrowser.setAlignment(QtCore.Qt.AlignLeft)
self.ui.textBrowser.setTextColor(QtCore.Qt.red)
self.ui.textBrowser.insertPlainText(text + '\n')
self.ui.textBrowser.repaint()
self.repaint()
if __name__ == '__main__':
app = QtGui.QApplication(sys.argv)
myapp = runGUI()
myapp.show()
sys.exit(app.exec_())