def extractWords(wordfile):
lines, _ = wordio.read(wordfile)
assert pth.exists('../crops/')
word_array = []
for line_idx, line in enumerate(lines):
for word_idx, word in enumerate(line):
temp_word = ""
for char_idx, char in enumerate(word.characters):
if (re.match("^[a-zA-Z]", char.text)):
char.text = char.text.decode('utf-8', 'ignore').encode("utf-8").lower()
temp_word = temp_word + char.text
word_array.append(temp_word)
return word_array
def extractImages(wordfile, imgfile, l_file, monogram, bigram, trigram):
print wordfile
lines, _ = wordio.read(wordfile)
img = Image.open(imgfile)
#img = pamImage.PamImage(imgfile)
#line_iter = iter(lines)
#cur_line = line_iter.next()
#word_iter = iter(cur_line)
assert pth.exists('../crops/')
for line_idx, line in enumerate(lines):
for word_idx, word in enumerate(line):
previous = ""
second_previous = ""
for idx, char in enumerate(word.characters):
char.text = char.text.decode('utf-8', 'ignore').encode("utf-8").lower()
if(re.match("^[a-zA-Z]",char.text)):
if char.text in monogram:
monogram[char.text] += 1
else:
monogram[char.text] = 1
if idx>0 and previous!="":
if char.text+previous in bigram:
bigram[char.text+previous] +=1
else:
bigram[char.text+previous] = 1
if idx>1 and second_previous!="":
if char.text+previous+second_previous in trigram:
trigram[char.text + previous + second_previous] += 1
else:
trigram[char.text+previous+second_previous] = 1
else:
previous = char.text
second_previous = previous
else:
previous = char.text
else:
previous = ""
second_previous = ""
return monogram,bigram,trigram
def extractImages(wordfile, imgfile, l_file):
print wordfile
lines, _ = wordio.read(wordfile)
img = Image.open(imgfile)
#img = pamImage.PamImage(imgfile)
#line_iter = iter(lines)
#cur_line = line_iter.next()
#word_iter = iter(cur_line)
assert pth.exists('../crops/')
out_str = '../crops/' + pth.basename(imgfile)
out_str = out_str.replace(IM_EXT, '')
for line_idx, line in enumerate(lines):
for word_idx, word in enumerate(line):
cropped = img.crop(
(word.left, word.top, word.right, word.bottom)
) # croplib.crop(img, word.left, word.top, word.right, word.bottom)
out_path = out_str + '_l' + str(line_idx) + '_w' + str(
word_idx) + '_t_' + word.text + IM_EXT
cropped.save(out_path.replace('/crops', '/crops/words'))
for idx, char in enumerate(word.characters):
cropped = img.crop(
(char.left, char.top, char.right, char.bottom)
) # croplib.crop(img, word.left, word.top, word.right, word.bottom)
if 0 in cropped.size:
continue
#print cropped.size
otsu_img = img_as_ubyte(
np.copy(np.asarray(cropped.convert('L'))))
try:
threshold_global_otsu = threshold_otsu(otsu_img)
except TypeError:
print 'Something weird happened'
continue
global_otsu = otsu_img >= threshold_global_otsu
l_otsu_im = 1 - global_otsu
index_row = np.tile(
np.arange(otsu_img.shape[0]).reshape(otsu_img.shape[0], 1),
(1, otsu_img.shape[1]))
index_col = np.tile(np.arange(otsu_img.shape[1]),
(otsu_img.shape[0], 1))
non0 = l_otsu_im.nonzero()
Wrow = np.multiply(l_otsu_im[non0], index_row[non0])
Wcol = np.multiply(l_otsu_im[non0], index_col[non0])
Mrow = np.mean(Wrow)
Mcol = np.mean(Wcol)
Std_row = np.std(Wrow)
Std_col = np.std(Wcol)
top = max(0, int(Mrow - 3 * Std_row))
bottom = min(cropped.size[1], int(Mrow + 3 * Std_row))
left = max(0, int(Mcol - 3 * Std_col))
right = min(cropped.size[0], int(Mcol + 3 * Std_col))
sub_cropped = cropped.crop((left, top, right, bottom))
char_string = char.text #.lower() #.lower() #if char.text is not '\\' else 'bb'
if char_string not in 'abcdefghijklmnopqrstuvwxyz&#*\\ABCDEFGHJIKLMNOPQRSTUVWXYZ' or char_string == '':
continue
out_path = out_str + '_l' + str(line_idx) + '_w' + str(
word_idx) + '_c' + str(idx) + '_t_' + char_string + IM_EXT
try:
sub_cropped.save(
out_path.replace('/crops', '/crops/letters'))
except SystemError:
print 'bad annotation...'
if char_string in word_log:
word_log[char_string] += 1
else:
word_log[char_string] = 1
#if char_string in label_dict:
#l_file.write(out_path + ' ' + str(ord(char_string) - ord('a')) + '\n')
l_file.write(out_path + ' ' +
str(label_mapping.index(char_string)) + ' \n')
def extractImages(wordfile, imgfile):
print wordfile
lines, _ = wordio.read(wordfile)
img = Image.open(imgfile)
#img = pamImage.PamImage(imgfile
# line_iter = iter(lines)
# cur_line = line_iter.next()
# word_iter = iter(cur_line)
assert pth.exists('../crops/')
out_str = '../crops/' + pth.basename(imgfile)
out_str = out_str.replace(IM_EXT, '')
train_y = []
for line_idx, line in enumerate(lines):
for word_idx, word in enumerate(line):
cropped = img.crop(
(word.left, word.top, word.right, word.bottom)
) # croplib.crop(img, word.left, word.top, word.right, word.bottom)
dict_name = out_str + '_l' + str(line_idx) + '_w' + str(
word_idx) + '_t_' + word.text
out_path = out_str + '_l' + str(line_idx) + '_w' + str(
word_idx) + '_t_' + word.text + IM_EXT
word_array = np.zeros((word.right - word.left))
gaussian_array = {}
cropped.save(out_path)
for idx, char in enumerate(word.characters):
# if(char.left - word.left >= 0):e
if (char.left - word.left < 0):
word_array[0] = 1
elif ((char.left - word.left) >= (word.right - word.left)):
word_array[(char.left - word.left) - 1] = 1
else:
word_array[(char.left - word.left)] = 1
word_array[(word.right - word.left) - 1] = 1
gaussian_array[dict_name] = gaussian_filter1d(word_array, 0.5)
# pyplot.plot(gaussian_array)
# pyplot.show()
train_y.append(gaussian_array)
"""
if 0 in cropped.size:
continue
# print cropped.size
otsu_img = img_as_ubyte(np.copy(np.asarray(cropped.convert('L'))))
try:
threshold_global_otsu = threshold_otsu(otsu_img)
except TypeError:
print 'Something weird happened'
continue
global_otsu = otsu_img >= threshold_global_otsu
l_otsu_im = 1 - global_otsu
index_row = np.tile(np.arange(otsu_img.shape[0]).reshape(otsu_img.shape[0], 1), (1, otsu_img.shape[1]))
index_col = np.tile(np.arange(otsu_img.shape[1]), (otsu_img.shape[0], 1))
non0 = l_otsu_im.nonzero()
Wrow = np.multiply(l_otsu_im[non0], index_row[non0])
Wcol = np.multiply(l_otsu_im[non0], index_col[non0])
Mrow = np.mean(Wrow)
Mcol = np.mean(Wcol)
Std_row = np.std(Wrow)
Std_col = np.std(Wcol)
top = max(0, int(Mrow - 3 * Std_row))
bottom = min(cropped.size[1], int(Mrow + 3 * Std_row))
left = max(0, int(Mcol - 3 * Std_col))
right = min(cropped.size[0], int(Mcol + 3 * Std_col))
sub_cropped = cropped.crop((left, top, right, bottom))
out_path = out_str + '_l' + str(line_idx) + '_w' + str(word_idx) + '_c' + str(
idx) + '_t_' + char.text + IM_EXT
try:
sub_cropped.save(out_path)
except SystemError:
print 'bad annotation...'
if char.text in word_log:
word_log[char.text.lower()] += 1
else:
word_log[char.text.lower()] = 1
if char.text.lower() in label_dict:
l_file.wri te(out_str + ' ' + str(label_dict[char.text.lower()] - 1) + '\n')
"""
return train_y
def __init__(self, word_file, img_file, out_file):
self.out_file = out_file
win = gtk.Window(gtk.WINDOW_TOPLEVEL)
win.set_size_request(500, 250)
win.connect('destroy', lambda w: gtk.main_quit())
vbox = gtk.VBox()
self.new_lines = [[]] # store the annotated words + chars here
self.lines, _ = wordio.read(word_file)
self.img = pamImage.PamImage(img_file)
# Keep track of where we are in the file
self.line_iter = iter(self.lines)
self.cur_line = self.line_iter.next()
self.word_iter = iter(self.cur_line)
self.pb = None
self.word = None
self.cropped = None
# Cursor and annotation-points (x-coordinates)
self.current_x = 0
self.points = []
# Drawing area to draw the lines on
self.drawing_area = gtk.DrawingArea()
self.drawing_area.connect("expose-event", self.on_expose)
self.drawing_area.connect("motion_notify_event", self.on_motion)
self.drawing_area.connect("button_press_event",
self.on_mouse_button_press)
self.drawing_area.set_events(gtk.gdk.EXPOSURE_MASK
| gtk.gdk.LEAVE_NOTIFY_MASK
| gtk.gdk.BUTTON_PRESS_MASK
| gtk.gdk.POINTER_MOTION_MASK
| gtk.gdk.POINTER_MOTION_HINT_MASK)
vbox.pack_start(self.drawing_area)
hbox = gtk.HBox()
vbox.pack_start(hbox)
# Label row
label = gtk.Label()
label.set_text("Characters labeled above:")
hbox.pack_start(label)
self.entry = gtk.Entry()
hbox.pack_start(self.entry)
# control button row
self.reset_button = gtk.Button("Reset")
self.next_button = gtk.Button("Next")
self.reset_button.connect("clicked", self.on_reset)
self.next_button.connect("clicked", self.on_next)
hbox = gtk.HBox()
hbox.pack_start(self.reset_button)
hbox.pack_start(self.next_button)
vbox.pack_start(hbox)
self.next_word()
win.add(vbox)
win.show_all()
def process_file(wordfile, imgfile):
n_words = matches = 0
lines, _ = wordio.read(wordfile)
img = Image.open(imgfile)
for line_idx, line in enumerate(lines):
for word_idx, word in enumerate(line):
n_words += 1
cropped = img.crop((word.left, word.top, word.right, word.bottom))
color = np.copy(np.asarray(cropped))
monograms, start, end = get_monograms_otsu(color, cropped)
if monograms is None:
continue
for mg in monograms:
if model == 'VGG':
im = 255 - mg.im[:, :, ::-1]
im[:, :, 0] -= 103.939
im[:, :, 1] -= 116.779
im[:, :, 2] -= 123.68
im = im.transpose((2, 0, 1))
convnet_output = conv_model.predict(im.reshape((1, im.shape[0], im.shape[1], im.shape[2])))
features = np.concatenate(
(np.max(convnet_output[:, :, :convnet_output.shape[2] / 2, :], axis=2),
np.max(convnet_output[:, :, convnet_output.shape[2] / 2:, :], axis=2)),
axis=1).transpose((0, 2, 1))
feature_seq, _ = pad_sequences([features], maxlen=31, dim=1024)
lstm_output = lstm_model.predict_classes([feature_seq, feature_seq], batch_size=1, verbose=0)
letter = chr(lstm_output[0] + ord('a'))
# print letter
mg.set_prediction(letter)
else:
width, height = 30, 55
im = np.asarray(255 - mg.im, dtype=np.float32)
im /= 255.
im = scipy.misc.imresize(im, (height, im.shape[1], im.shape[2]))
# if im.shape[1] <= width :
# im = np.pad(im, ((0,0), (0,width-im.shape[1]), (0,0)), mode='constant', constant_values=0)
# else :
im = scipy.misc.imresize(im, (im.shape[0], width, im.shape[2]))
# plt.imshow(im)
# plt.show()
im = np.asarray(im, dtype=np.float32)
# im = im[:, :, :] - 126
im /= 255.
im = im.transpose((2, 0, 1))
conv_output = conv_model.predict_classes(im.reshape((1, im.shape[0], im.shape[1], im.shape[2])),
batch_size=1, verbose=0)
conv_confidences = conv_model.predict(im.reshape((1, im.shape[0], im.shape[1], im.shape[2])),
batch_size=1, verbose=0)
letter = label_mapping[conv_output[0]] #chr(conv_output[0] + ord('a'))
mg.set_prediction(letter)
mg.set_confidence(conv_confidences[0][conv_output[0]])
if len(monograms) < 50: # So that it isn't that slow/freezes
threshold = 0.1
convident_idcs = conv_confidences > threshold
#charlist = label_mapping #(convident_idcs * range(26))[convident_idcs] + ord('a')
char_idxs = (convident_idcs * range(len(label_mapping)))[convident_idcs]
print char_idxs
chars = np.asarray(label_mapping)[char_idxs] #np.asarray(label_mapping)[convident_idcs]#[chr(char) for char in charlist]
confs = (conv_confidences)[convident_idcs]
options = zip(chars, confs)
mg.set_options(options)
else:
mg.set_options([(letter, mg.get_confidence())])
N_grams = monograms
words = []
word_prediction = ''
print 'Target: ', word.text,
def build_words(wrd, N_grams, start, end):
if wrd is None:
for g in N_grams:
if g.start == start:
build_words(g, N_grams, start, end)
return
if wrd.end == end:
words.append(wrd)
return
for idx, g in enumerate(N_grams):
if wrd.followed_by(g):
build_words(wrd.combine(g), N_grams[idx:], start, end)
def build_words2(wrd, N_grams, start, end):
if wrd is None:
for g in N_grams:
if g.start == start:
for char, conf in g.get_options():
cpy = N_gram(g.im, g.start, g.end, char, conf)
build_words2(cpy, N_grams, start, end)
return
if wrd.end == end:
words.append(wrd)
return
for idx, g in enumerate(N_grams):
if wrd.followed_by(g):
for char, conf in g.get_options():
cpy = N_gram(g.im, g.start, g.end, char, conf)
build_words2(wrd.combine(cpy), N_grams[idx:], start, end)
build_words2(None, N_grams, start, end)
words = sorted(words, key=lambda w: w.get_confidence())[::-1]
# for w in words[:10]:
# print w.prediction, w.get_confidence()
if len(words) == 0:
print ''
continue
word_strings = [wrd.prediction for wrd in words]
for wrd in word_strings:
if wrd in vocabulary:
word_prediction = wrd
break
if word_prediction == '':
words = []
def build_words(wrd, N_grams, start, end):
if wrd is None:
for g in N_grams:
if g.start == start:
build_words(g, N_grams, start, end)
return
if wrd.end == end:
words.append(wrd)
return
for idx, g in enumerate(N_grams):
if wrd.followed_by(g):
build_words(wrd.combine(g), N_grams[idx:], start, end)
build_words(None, N_grams, start, end)
words = sorted(words, key=lambda w: w.get_confidence())[::-1]
word_strings = [wrd.prediction for wrd in words[:10]]
word_exists = checkWordInNgrams(word_strings, ngram_voc)
if len(word_exists) == 1:
word_prediction = word_exists[0]
else:
lev = calculateDistance(word_strings, vocabulary)
# print type(lev)
lev = sorted(lev.items(), key=operator.itemgetter(1))
counter = 0
# for k, v in lev[:10]:
# print "word:" + k + " with score:" + str(v)
if lev == {}:
if len(word_exists) >= 1:
word_prediction = word_exists[0]
else:
word_prediction = words[0].prediction
else:
closest = []
minDist = min([l[1] for l in lev])
for k, v in lev:
if v == minDist:
closest.append(k)
if len(closest) == 1:
word_prediction = closest[0]
else:
maxConf = 0
final_prediction = ''
for c in closest:
for w in words:
if w.prediction == c and maxConf < w.get_confidence():
maxConf = w.get_confidence()
final_prediction = w.prediction
if final_prediction == '':
if len(word_exists) >= 1:
word_prediction = word_exists[0]
else:
word_prediction = words[0].prediction
else:
llev = calculateDistance([final_prediction], vocabulary)
llev = sorted(llev.items(), key=operator.itemgetter(1))
# print 'dictieeeee', llev[0][0]
word_prediction = llev[0][0]
print ', prediction: ', word_prediction
matches += 1 if word.text.replace(';',"").replace('~','').replace('valde_','').replace('Ottonenem', '').replace('ducem', '').replace('@','').replace('^o', '').replace('@ampersand', '&').replace(']', '') == word_prediction else 0
return matches, n_words
def extractImages(wordfile, imgfile, l_file):
print wordfile
lines, _ = wordio.read(wordfile)
img = Image.open(imgfile)
#img = pamImage.PamImage(imgfile)
#line_iter = iter(lines)
#cur_line = line_iter.next()
#word_iter = iter(cur_line)
assert pth.exists('../crops/')
out_str = '../crops/' + pth.basename(imgfile)
out_str = out_str.replace(IM_EXT, '')
for line_idx, line in enumerate(lines):
for word_idx, word in enumerate(line):
cropped = img.crop((word.left, word.top, word.right, word.bottom)) # croplib.crop(img, word.left, word.top, word.right, word.bottom)
out_path = out_str + '_l' + str(line_idx) + '_w' + str(word_idx) + '_t_' + word.text + IM_EXT
cropped.save(out_path.replace('/crops', '/crops/words'))
for idx, char in enumerate(word.characters):
cropped = img.crop((char.left, char.top, char.right, char.bottom)) # croplib.crop(img, word.left, word.top, word.right, word.bottom)
if 0 in cropped.size:
continue
#print cropped.size
otsu_img = img_as_ubyte(np.copy(np.asarray(cropped.convert('L'))))
try:
threshold_global_otsu = threshold_otsu(otsu_img)
except TypeError:
print 'Something weird happened'
continue
global_otsu = otsu_img >= threshold_global_otsu
l_otsu_im = 1 - global_otsu
index_row = np.tile(np.arange(otsu_img.shape[0]).reshape(otsu_img.shape[0], 1), (1, otsu_img.shape[1]))
index_col = np.tile(np.arange(otsu_img.shape[1]), (otsu_img.shape[0], 1))
non0 = l_otsu_im.nonzero()
Wrow = np.multiply(l_otsu_im[non0], index_row[non0])
Wcol = np.multiply(l_otsu_im[non0], index_col[non0])
Mrow = np.mean(Wrow)
Mcol = np.mean(Wcol)
Std_row = np.std(Wrow)
Std_col = np.std(Wcol)
top = max(0, int(Mrow - 3 * Std_row))
bottom = min(cropped.size[1], int(Mrow + 3 * Std_row))
left = max(0, int(Mcol - 3 * Std_col))
right = min(cropped.size[0], int(Mcol + 3 * Std_col))
sub_cropped = cropped.crop((left, top, right, bottom))
char_string = char.text #.lower() #.lower() #if char.text is not '\\' else 'bb'
if char_string not in 'abcdefghijklmnopqrstuvwxyz&#*\\ABCDEFGHJIKLMNOPQRSTUVWXYZ' or char_string == '':
continue
out_path = out_str + '_l' + str(line_idx) + '_w' + str(word_idx) + '_c' + str(idx) + '_t_' + char_string + IM_EXT
try:
sub_cropped.save(out_path.replace('/crops', '/crops/letters'))
except SystemError:
print 'bad annotation...'
if char_string in word_log:
word_log[char_string] += 1
else:
word_log[char_string] = 1
#if char_string in label_dict:
#l_file.write(out_path + ' ' + str(ord(char_string) - ord('a')) + '\n')
l_file.write(out_path + ' ' + str(label_mapping.index(char_string)) + ' \n')
def extractImages(wordfile, imgfile):
print wordfile
lines, _ = wordio.read(wordfile)
img = Image.open(imgfile)
#img = pamImage.PamImage(imgfile
# line_iter = iter(lines)
# cur_line = line_iter.next()
# word_iter = iter(cur_line)
assert pth.exists('../crops/')
out_str = '../crops/' + pth.basename(imgfile)
out_str = out_str.replace(IM_EXT, '')
train_y = []
for line_idx, line in enumerate(lines):
for word_idx, word in enumerate(line):
cropped = img.crop((word.left, word.top, word.right, word.bottom)) # croplib.crop(img, word.left, word.top, word.right, word.bottom)
dict_name = out_str +'_l' + str(line_idx) + '_w' + str(word_idx) + '_t_' + word.text
out_path = out_str + '_l' + str(line_idx) + '_w' + str(word_idx) + '_t_' + word.text + IM_EXT
word_array = np.zeros((word.right - word.left))
gaussian_array = {}
cropped.save(out_path)
for idx, char in enumerate(word.characters):
# if(char.left - word.left >= 0):e
if(char.left - word.left < 0):
word_array[0] = 1
elif((char.left-word.left)>=(word.right-word.left)):
word_array[(char.left - word.left)-1] = 1
else:
word_array[(char.left - word.left)] = 1
word_array[(word.right-word.left)-1] = 1
gaussian_array[dict_name] = gaussian_filter1d(word_array,0.5)
# pyplot.plot(gaussian_array)
# pyplot.show()
train_y.append(gaussian_array)
"""
if 0 in cropped.size:
continue
# print cropped.size
otsu_img = img_as_ubyte(np.copy(np.asarray(cropped.convert('L'))))
try:
threshold_global_otsu = threshold_otsu(otsu_img)
except TypeError:
print 'Something weird happened'
continue
global_otsu = otsu_img >= threshold_global_otsu
l_otsu_im = 1 - global_otsu
index_row = np.tile(np.arange(otsu_img.shape[0]).reshape(otsu_img.shape[0], 1), (1, otsu_img.shape[1]))
index_col = np.tile(np.arange(otsu_img.shape[1]), (otsu_img.shape[0], 1))
non0 = l_otsu_im.nonzero()
Wrow = np.multiply(l_otsu_im[non0], index_row[non0])
Wcol = np.multiply(l_otsu_im[non0], index_col[non0])
Mrow = np.mean(Wrow)
Mcol = np.mean(Wcol)
Std_row = np.std(Wrow)
Std_col = np.std(Wcol)
top = max(0, int(Mrow - 3 * Std_row))
bottom = min(cropped.size[1], int(Mrow + 3 * Std_row))
left = max(0, int(Mcol - 3 * Std_col))
right = min(cropped.size[0], int(Mcol + 3 * Std_col))
sub_cropped = cropped.crop((left, top, right, bottom))
out_path = out_str + '_l' + str(line_idx) + '_w' + str(word_idx) + '_c' + str(
idx) + '_t_' + char.text + IM_EXT
try:
sub_cropped.save(out_path)
except SystemError:
print 'bad annotation...'
if char.text in word_log:
word_log[char.text.lower()] += 1
else:
word_log[char.text.lower()] = 1
if char.text.lower() in label_dict:
l_file.wri te(out_str + ' ' + str(label_dict[char.text.lower()] - 1) + '\n')
"""
return train_y
def __init__(self, word_file, img_file, out_file):
self.out_file = out_file
win = gtk.Window(gtk.WINDOW_TOPLEVEL)
win.set_size_request(500, 250)
win.connect('destroy', lambda w: gtk.main_quit())
vbox = gtk.VBox()
self.new_lines = [[]] # store the annotated words + chars here
self.lines, _ = wordio.read(word_file)
self.img = pamImage.PamImage(img_file)
# Keep track of where we are in the file
self.line_iter = iter(self.lines)
self.cur_line = self.line_iter.next()
self.word_iter = iter(self.cur_line)
self.pb = None
self.word = None
self.cropped = None
# Cursor and annotation-points (x-coordinates)
self.current_x = 0
self.points = []
# Drawing area to draw the lines on
self.drawing_area = gtk.DrawingArea()
self.drawing_area.connect("expose-event", self.on_expose)
self.drawing_area.connect("motion_notify_event", self.on_motion)
self.drawing_area.connect("button_press_event",
self.on_mouse_button_press)
self.drawing_area.set_events(gtk.gdk.EXPOSURE_MASK
| gtk.gdk.LEAVE_NOTIFY_MASK
| gtk.gdk.BUTTON_PRESS_MASK
| gtk.gdk.POINTER_MOTION_MASK
| gtk.gdk.POINTER_MOTION_HINT_MASK)
vbox.pack_start(self.drawing_area)
hbox = gtk.HBox()
vbox.pack_start(hbox)
# Label row
label = gtk.Label()
label.set_text("Characters labeled above:")
hbox.pack_start(label)
self.entry = gtk.Entry()
hbox.pack_start(self.entry)
# control button row
self.reset_button = gtk.Button("Reset")
self.next_button = gtk.Button("Next")
self.reset_button.connect("clicked", self.on_reset)
self.next_button.connect("clicked", self.on_next)
hbox = gtk.HBox()
hbox.pack_start(self.reset_button)
hbox.pack_start(self.next_button)
vbox.pack_start(hbox)
self.next_word()
win.add(vbox)
win.show_all()
def show_hists(wordfile, imgfile):
lines, _ = wordio.read(wordfile)
img = Image.open(imgfile)
for line_idx, line in enumerate(lines):
for word_idx, word in enumerate(line):
cropped = img.crop((word.left, word.top, word.right, word.bottom))
color = np.copy(np.asarray(cropped))
print color.shape
#color = augment(color)
hist = np.mean(color, axis=(0,2))
hist = np.maximum(np.mean(color[color.shape[0]/2:], axis=(0, 2)), hist)
hist = np.maximum(np.mean(color[:color.shape[0] / 2], axis=(0, 2)), hist)
hist = np.maximum(np.mean(color[color.shape[0]/4:-color.shape[0]/4], axis=(0, 2)), hist)
hist = np.maximum(np.mean(color[color.shape[0]/8:-3*color.shape[0]/8], axis=(0, 2)), hist)
hist = np.maximum(np.mean(color[3*color.shape[0]/8:-color.shape[0]/8], axis=(0, 2)), hist)
print hist.shape
otsu_img = img_as_ubyte(np.copy(np.asarray(cropped.convert('L'))))
try:
threshold_global_otsu = threshold_otsu(otsu_img)
except TypeError:
print 'Something weird happened'
continue
global_otsu = np.array(otsu_img >= threshold_global_otsu).astype(np.int64)
hist2 = np.zeros(global_otsu.shape[1])
for col in range(global_otsu.shape[1]):
max_white = 0
white = 0
for row in range(global_otsu.shape[0]):
white += 1 if global_otsu[row, col] == 1 else 0
max_white = max(white, max_white)
hist2[col] = max_white
plt.imshow(color)
smooth_win = 11
hist = hist2
#hist_small = np.convolve(hist, 5 * [1 / 5.], 'same')
hist = np.convolve(hist, 5 * [1/5.], 'same') / 25
#hist[:smooth_win-1] = hist_small[:smooth_win-1]
#hist[-(smooth_win-1):] = hist_small[-(smooth_win-1):]
plt.bar(np.arange(color.shape[1]), hist)
plt.title(word.text)
for idx in range(1, len(hist)-1):
if hist[idx] == hist[idx+1] and hist[idx-1] < hist[idx]:
hist[idx] = (hist[idx+1] + hist[idx-1]) / 2
maxes = argrelextrema(hist, np.greater)
med = np.median(hist)
cuts = maxes[0]
'''
for idx in range(len(cuts) - 1):
#while len(cuts) > idx + 1 and hist[cuts[idx]] < med:
# cuts = np.delete(cuts, idx)
while len(cuts) > idx + 1 and cuts[idx + 1] - cuts[idx] < 10:
cuts[idx] = cuts[idx] if hist[cuts[idx]] > hist[cuts[idx+1]] else cuts[idx]
cuts = np.delete(cuts, idx+1)
'''
plt.bar(cuts, len(cuts) * [color.shape[0]], color='r')
print hist
plt.xticks([])
plt.yticks([])
plt.tight_layout()
plt.show()
monograms = []
for idx, cut in enumerate(cuts):
for next in cuts[idx+1:]:
print next, cut
if 10 < next - cut < 50:
monograms.append(Monogram(color[:, cut:next], cut, next))