def test_init_method_different_parameters(self):
test_dir = File("../../character_examples").getPath()
nr_of_training_examples = 90
nr_of_test_examples = 10
for size_classification_factor in drange(0.7, 6.0, 0.3):
print str(size_classification_factor) + ' &',
for nr_of_segs in range(4, 13):
#print(nr_of_segs)
test_scores = []
for test_nr in range(10):
#print(test_nr)
extracor = SimpleImageFeatureExtractor(
nr_of_divisions=nr_of_segs,
size_classification_factor=size_classification_factor)
training_examples, test_examples = extracor.extract_training_and_test_examples(
test_dir, nr_of_training_examples, nr_of_test_examples)
classifier = CharacterClassifier(
training_examples,
nr_of_hmms_to_try=1,
fraction_of_examples_for_test=0,
train_with_examples=False,
initialisation_method=SpecializedHMM.InitMethod.
count_based)
test_scores.append(classifier.test(test_examples))
score = sum(test_scores) / len(test_scores)
print ' $' + str(score) + '$ ',
if nr_of_segs == 12:
print '\\\\'
else:
print '&',
def create_character_classification_count_matrix():
'''
This function do the following:
1. Creates a character classifier with 90 training examples
2. Runs a test with 10 test examples for every character. The results are put in a
matrix M so element M[i][j] contains the number of test examples for the character
with index i that are classified to be the character with index j.
'''
extracor = SimpleImageFeatureExtractor(nr_of_divisions=11,
size_classification_factor=3.4)
training_examples, test_examples = extracor.extract_training_and_test_examples(File(File(File(".."),".."),"character_examples").getCanonicalPath(),
nr_of_training_examples=90,
nr_of_test_examples=10)
classifier = CharacterClassifier(training_examples,
nr_of_hmms_to_try=1,
fraction_of_examples_for_test=0,
train_with_examples=False,
initialisation_method=SpecializedHMM.InitMethod.count_based)
alphabet = get_example_alphabet()
classification_count_matrix = zeros(len(alphabet),len(alphabet))
for label, examples in test_examples:
character = label.lower()
character_index = alphabet.index(character)
for example in examples:
classified_character = classifier.classify_character_string(example).lower()
classified_character_index = alphabet.index(classified_character)
count = classification_count_matrix[character_index][classified_character_index]
classification_count_matrix[character_index][classified_character_index] = count + 1
return classification_count_matrix
def test_init_method_different_parameters(self):
test_dir = File("../../character_examples").getPath()
nr_of_training_examples = 90
nr_of_test_examples = 10
for size_classification_factor in drange(0.7, 6.0, 0.3):
print str(size_classification_factor) + ' &',
for nr_of_segs in range(4,13):
#print(nr_of_segs)
test_scores = []
for test_nr in range(10):
#print(test_nr)
extracor = SimpleImageFeatureExtractor(nr_of_divisions=nr_of_segs,
size_classification_factor=size_classification_factor)
training_examples, test_examples = extracor.extract_training_and_test_examples(test_dir,
nr_of_training_examples,
nr_of_test_examples)
classifier = CharacterClassifier(training_examples,
nr_of_hmms_to_try=1,
fraction_of_examples_for_test=0,
train_with_examples=False,
initialisation_method=SpecializedHMM.InitMethod.count_based)
test_scores.append(classifier.test(test_examples))
score = sum(test_scores) / len(test_scores)
print ' $' + str(score) +'$ ',
if nr_of_segs == 12:
print '\\\\'
else:
print '&',
def create_character_classification_count_matrix():
'''
This function do the following:
1. Creates a character classifier with 90 training examples
2. Runs a test with 10 test examples for every character. The results are put in a
matrix M so element M[i][j] contains the number of test examples for the character
with index i that are classified to be the character with index j.
'''
extracor = SimpleImageFeatureExtractor(nr_of_divisions=11,
size_classification_factor=3.4)
training_examples, test_examples = extracor.extract_training_and_test_examples(
File(File(File(".."), ".."), "character_examples").getCanonicalPath(),
nr_of_training_examples=90,
nr_of_test_examples=10)
classifier = CharacterClassifier(
training_examples,
nr_of_hmms_to_try=1,
fraction_of_examples_for_test=0,
train_with_examples=False,
initialisation_method=SpecializedHMM.InitMethod.count_based)
alphabet = get_example_alphabet()
classification_count_matrix = zeros(len(alphabet), len(alphabet))
for label, examples in test_examples:
character = label.lower()
character_index = alphabet.index(character)
for example in examples:
classified_character = classifier.classify_character_string(
example).lower()
classified_character_index = alphabet.index(classified_character)
count = classification_count_matrix[character_index][
classified_character_index]
classification_count_matrix[character_index][
classified_character_index] = count + 1
return classification_count_matrix
def get_character_classifier_with_init_method(traing_examples,
init_method):
return CharacterClassifier(traing_examples,
nr_of_hmms_to_try=1,
fraction_of_examples_for_test=0,
train_with_examples=False,
initialisation_method=init_method)
def create_character_classifier(save_to_file_path):
example_dir = File("../../character_examples").getPath()
nr_of_training_examples = 90
nr_of_test_examples = 10
extractor = SimpleImageFeatureExtractor(nr_of_divisions=11,
size_classification_factor=4.6)
training_examples, test_examples = extractor.extract_training_and_test_examples(example_dir,
nr_of_training_examples,
nr_of_test_examples)
classifier = CharacterClassifier(training_examples,
nr_of_hmms_to_try=1,
fraction_of_examples_for_test=0,
train_with_examples=True,
initialisation_method=SpecializedHMM.InitMethod.count_based,
feature_extractor=extractor)
test_result = str(classifier.test(test_examples))
print('Prediction ratio:', test_result)
def create_character_classifier(save_to_file_path):
example_dir = File("../../character_examples").getPath()
nr_of_training_examples = 100
nr_of_test_examples = 0
extractor = SimpleImageFeatureExtractor(nr_of_divisions=7,
size_classification_factor=1.3)
training_examples, test_examples = extractor.extract_training_and_test_examples(example_dir,
nr_of_training_examples,
nr_of_test_examples)
classifier = CharacterClassifier(training_examples,
nr_of_hmms_to_try=1,
fraction_of_examples_for_test=0,
train_with_examples=False,
initialisation_method=SpecializedHMM.InitMethod.count_based,
feature_extractor=extractor)
classifier_string = classifier.to_string()
file = open(save_to_file_path,'w')
file.write(classifier_string)
file.close()
def get_character_classifier(self):
path_to_this_dir = File(str(inspect.getfile(
inspect.currentframe()))).getParent()
classifier_filename = (
"character_classifier_" +
str(self.feature_extraction_number_of_segments) + "_segments_cf_" +
str(self.feature_extraction_classification_factor).replace(
'.', '_') + ".dat")
character_classifier_file = open(
File(path_to_this_dir, classifier_filename).getPath(), 'r')
character_classifier = CharacterClassifier(
from_string_string=character_classifier_file.read())
character_classifier_file.close()
return character_classifier
def create_character_classifier(save_to_file_path):
example_dir = File("../../character_examples").getPath()
nr_of_training_examples = 100
nr_of_test_examples = 0
extractor = SimpleImageFeatureExtractor(nr_of_divisions=11,
size_classification_factor=4.6)
training_examples, test_examples = extractor.extract_training_and_test_examples(
example_dir, nr_of_training_examples, nr_of_test_examples)
classifier = CharacterClassifier(
training_examples,
nr_of_hmms_to_try=1,
fraction_of_examples_for_test=0,
train_with_examples=False,
initialisation_method=SpecializedHMM.InitMethod.count_based,
feature_extractor=extractor)
#test_result = str(classifier.test(test_examples))
#print(test_result)
classifier_string = classifier.to_string()
file = open(save_to_file_path + ".dat", 'w')
file.write(classifier_string)
file.close()
def __init__(self):
'''
Constructor
'''
#Create classifiers
#Character classifier
path_to_this_dir = File(str(inspect.getfile( inspect.currentframe() ))).getParent()
character_classifier_file = open(File(path_to_this_dir,"character_classifier.dat").getPath(),'r')
self.character_classifier = CharacterClassifier(from_string_string=character_classifier_file.read())
character_classifier_file.close()
#Word classifier
word_classifier_file = open(File(path_to_this_dir,"word_classifier.dat").getPath(),'r')
self.word_classifier= WordClassifier(from_string_string=word_classifier_file.read())
word_classifier_file.close()
#Set up window
self.setTitle("HandReco Writer")
self.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE)
self.setLocationRelativeTo(None)
self.setLayout(BorderLayout())
#Set up menu
menu_bar = JMenuBar()
info_menu = JMenu("Info")
def instructions(event):
instr = '''
The program can just recognise capital English characters.
See Info -> Available Words... for available word corrections.
Good Luck with the writing!'''
JOptionPane.showMessageDialog(self, instr,
"Instructions",
JOptionPane.INFORMATION_MESSAGE)
instructions_menu_item = JMenuItem("Instructions...",actionPerformed=instructions)
info_menu.add(instructions_menu_item)
def word_corrections(event):
words_string = ""
for word in self.word_classifier.words:
words_string = words_string + word.upper() + "\n"
text = "The words that can be corrected are:\n\n" +words_string
dialog = JOptionPane(text,
JOptionPane.INFORMATION_MESSAGE)
dialog_wrapper = JDialog(self,"Available Words",False)
dialog_wrapper.setContentPane(dialog)
dialog_wrapper.pack()
dialog_wrapper.setVisible(True)
word_corrections_menu_item = JMenuItem("Available Words...",actionPerformed=word_corrections)
info_menu.add(word_corrections_menu_item)
menu_bar.add(info_menu)
self.setJMenuBar(menu_bar)
#Input panel
input_panel = JPanel()
input_panel.setLayout(BoxLayout(input_panel, BoxLayout.X_AXIS))
input_panel.setBorder(BorderFactory.createTitledBorder("Input"))
self.paint_area = PaintArea(100,100)
input_panel.add(self.paint_area)
input_options_panel = JPanel()
input_options_panel.setLayout(BoxLayout(input_options_panel, BoxLayout.Y_AXIS))
#Write Char
write_char_panel = JPanel(BorderLayout())
def write_char(event):
char = self.character_classifier.classify_image(self.paint_area.image())
self.text_area.setText(self.text_area.getText() + char)
self.paint_area.clear()
write_char_panel.add(JButton("Write Char", actionPerformed=write_char), BorderLayout.NORTH)
input_options_panel.add(write_char_panel)
#Space and Correct
space_and_correct_panel = JPanel(BorderLayout())
def space_and_correct(event):
text = self.text_area.getText()
words = text.split(" ")
string = words[-1]
try:
word = self.word_classifier.classify(string.lower())
words[-1] = word.upper()
except:
JOptionPane.showMessageDialog(self, "Have you entered a character which is not in the alphabet?",
"Could not Correct",
JOptionPane.ERROR_MESSAGE)
self.text_area.setText(text + " ")
return
newText = ""
for w in words:
newText = newText + w + " "
self.text_area.setText(newText)
space_and_correct_panel.add(JButton("Space and Correct", actionPerformed=space_and_correct), BorderLayout.NORTH)
input_options_panel.add(space_and_correct_panel)
#Space
space_panel = JPanel(BorderLayout())
def space(event):
self.text_area.setText(self.text_area.getText() + " ")
space_panel.add(JButton("Space", actionPerformed=space), BorderLayout.NORTH)
input_options_panel.add(space_panel)
#Clear Input Area
clear_input_area_panel = JPanel(BorderLayout())
def clear(event):
self.paint_area.clear()
clear_input_area_panel.add(JButton("Clear Input Area", actionPerformed=clear), BorderLayout.NORTH)
input_options_panel.add(clear_input_area_panel)
input_panel.add(input_options_panel)
self.add(input_panel, BorderLayout.NORTH)
text_area_panel = JPanel()
text_area_panel.setLayout(BorderLayout())
text_area_panel.setBorder(BorderFactory.createTitledBorder("Text"))
self.text_area = JTextArea()
self.text_area.setLineWrap(True)
#Increase font size
font = self.text_area.getFont()
self.text_area.setFont(Font(font.getName(), Font.BOLD, 24))
text_area_panel.add(JScrollPane(self.text_area), BorderLayout.CENTER)
self.add(text_area_panel, BorderLayout.CENTER)
self.pack()
self.setSize(Dimension(300,300))
self.setVisible(True)
def __init__(self):
'''
Constructor
'''
#Create classifiers
#Character classifier
path_to_this_dir = File(str(inspect.getfile(
inspect.currentframe()))).getParent()
character_classifier_file = open(
File(path_to_this_dir,
"character_classifier_11_segments_4_6_cf.dat").getPath(), 'r')
self.character_classifier = CharacterClassifier(
from_string_string=character_classifier_file.read())
character_classifier_file.close()
#Word classifier
word_classifier_file = open(
File(path_to_this_dir, "word_classifier.dat").getPath(), 'r')
self.word_classifier = WordClassifier(
from_string_string=word_classifier_file.read())
word_classifier_file.close()
#Set up window
self.setTitle("HandReco Writer")
self.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE)
self.setLocationRelativeTo(None)
self.setLayout(BorderLayout())
#Set up menu
menu_bar = JMenuBar()
info_menu = JMenu("Info")
def instructions(event):
instr = '''
The program can just recognise capital English characters.
See Info -> Available Words... for available word corrections.
Good Luck with the writing!'''
JOptionPane.showMessageDialog(self, instr, "Instructions",
JOptionPane.INFORMATION_MESSAGE)
instructions_menu_item = JMenuItem("Instructions...",
actionPerformed=instructions)
info_menu.add(instructions_menu_item)
def word_corrections(event):
words_string = ""
for word in self.word_classifier.words:
words_string = words_string + word.upper() + "\n"
text = "The words that can be corrected are:\n\n" + words_string
dialog = JOptionPane(text, JOptionPane.INFORMATION_MESSAGE)
dialog_wrapper = JDialog(self, "Available Words", False)
dialog_wrapper.setContentPane(dialog)
dialog_wrapper.pack()
dialog_wrapper.setVisible(True)
word_corrections_menu_item = JMenuItem(
"Available Words...", actionPerformed=word_corrections)
info_menu.add(word_corrections_menu_item)
menu_bar.add(info_menu)
self.setJMenuBar(menu_bar)
#Input panel
input_panel = JPanel()
input_panel.setLayout(BoxLayout(input_panel, BoxLayout.X_AXIS))
input_panel.setBorder(BorderFactory.createTitledBorder("Input"))
self.paint_area = PaintArea(100, 100)
input_panel.add(self.paint_area)
input_options_panel = JPanel()
input_options_panel.setLayout(
BoxLayout(input_options_panel, BoxLayout.Y_AXIS))
#Write Char
write_char_panel = JPanel(BorderLayout())
def write_char(event):
char = self.character_classifier.classify_image(
self.paint_area.image())
self.text_area.setText(self.text_area.getText() + char)
self.paint_area.clear()
write_char_panel.add(JButton("Write Char", actionPerformed=write_char),
BorderLayout.NORTH)
input_options_panel.add(write_char_panel)
#Space and Correct
space_and_correct_panel = JPanel(BorderLayout())
def space_and_correct(event):
text = self.text_area.getText()
words = text.split(" ")
string = words[-1]
try:
word = self.word_classifier.classify(string.lower())
words[-1] = word.upper()
except:
JOptionPane.showMessageDialog(
self,
"Have you entered a character which is not in the alphabet?",
"Could not Correct", JOptionPane.ERROR_MESSAGE)
self.text_area.setText(text + " ")
return
newText = ""
for w in words:
newText = newText + w + " "
self.text_area.setText(newText)
space_and_correct_panel.add(
JButton("Space and Correct", actionPerformed=space_and_correct),
BorderLayout.NORTH)
input_options_panel.add(space_and_correct_panel)
#Space
space_panel = JPanel(BorderLayout())
def space(event):
self.text_area.setText(self.text_area.getText() + " ")
space_panel.add(JButton("Space", actionPerformed=space),
BorderLayout.NORTH)
input_options_panel.add(space_panel)
#Clear Input Area
clear_input_area_panel = JPanel(BorderLayout())
def clear(event):
self.paint_area.clear()
clear_input_area_panel.add(
JButton("Clear Input Area", actionPerformed=clear),
BorderLayout.NORTH)
input_options_panel.add(clear_input_area_panel)
input_panel.add(input_options_panel)
self.add(input_panel, BorderLayout.NORTH)
text_area_panel = JPanel()
text_area_panel.setLayout(BorderLayout())
text_area_panel.setBorder(BorderFactory.createTitledBorder("Text"))
self.text_area = JTextArea()
self.text_area.setLineWrap(True)
#Increase font size
font = self.text_area.getFont()
self.text_area.setFont(Font(font.getName(), Font.BOLD, 24))
text_area_panel.add(JScrollPane(self.text_area), BorderLayout.CENTER)
self.add(text_area_panel, BorderLayout.CENTER)
self.pack()
self.setSize(Dimension(300, 300))
self.setVisible(True)
