def text_to_image(title):
word_list = []
title_roman = romanize(title).lower()
title_roman_wo_space = re.sub(
r" ", r"_-",
re.sub(r"[`;<>~0-9\[\]|:+-/.,!?@#$%^*()\'\"]", r" ",
re.sub(r"&", r" and ", title_roman)))
NPOINTS = MAX_LENGTH + 1
x = []
y = []
len_title_roman = len(title_roman_wo_space)
if len_title_roman < MAX_LENGTH:
spare_space = MAX_LENGTH - len_title_roman
for a in (str("-") + title_roman_wo_space + str("_" *
(spare_space + 2))):
try:
word_list.append(phone_dict[a])
except:
pass
for i in range(len(word_list) - 1):
x_, y_ = word_list[i], word_list[i + 1]
i += 1
x.append(x_)
y.append(y_)
plt.figure(figsize=(2.24, 2.24))
for i in range(NPOINTS - 1):
plt.plot(x[i:i + 2],
y[i:i + 2],
alpha=float(NPOINTS - 1 - i) / (NPOINTS - 1),
linewidth=5 * math.log10(1000. / len_title_roman) *
float(NPOINTS - 1 - i) / (NPOINTS - 1),
color='black')
plt.plot(47, 47, alpha=0.00000000001)
plt.axis('off')
filename = "image/" + title + ".png"
plt.savefig(filename)
img = cv2.imread(filename, 0)
img = (img * -1 + 255.) / 255.
scipy.misc.imsave(filename, img)
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
import tensorflow as tf
from data.text_to_img_2 import save_pair_image
from converter.romanize import romanize
import re
MAX_LENGTH = 20
LR = 0.001
IMG_SIZE = 224
MODEL_NAME = 'trademarks-{}-{}.model'.format(LR, '2conv-basic')
title1 = input("Title1 EN limit 20, KR limit 7:")
title1_roman = romanize(title1)
title1_input = title1_roman
title2 = input("Title2 EN limit 20, KR limit 7:")
title2_roman = romanize(title2)
title2_input = title2_roman
save_pair_image(title1_input, title2_input, 0)
title1_refine = re.sub(r"[`;&<>~0-9\[\]|:+-/.,!?@#$%^*()\'\"]", r"",
title1_input)
title2_refine = re.sub(r"[`;&<>~0-9\[\]|:+-/.,!?@#$%^*()\'\"]", r"",
title2_input)
path = 'image_test/' + str(title1_refine[:MAX_LENGTH]) + "." + str(title2_refine[:MAX_LENGTH]) + \
'.' + str(0) + '.png'
def textpair_to_image(title1, title2):
title1_roman = romanize(title1).lower()
title2_roman = romanize(title2).lower()
title1_roman_wo_space = re.sub(
r" ", r"_-",
re.sub(r"[`;<>~0-9\[\]|:+-/.,!?@#$%^*()\'\"]", r"",
re.sub(r"&", r" and ", title1_roman)))
title2_roman_wo_space = re.sub(
r" ", r"_-",
re.sub(r"[`;<>~0-9\[\]|:+-/.,!?@#$%^*()\'\"]", r"",
re.sub(r"&", r" and ", title2_roman)))
NPOINTS1 = MAX_LENGTH + 1
NPOINTS2 = MAX_LENGTH + 1
img3 = np.zeros([224, 224], dtype=np.float64)
word1_list = []
word2_list = []
x1 = []
y1 = []
x2 = []
y2 = []
len_title1_roman = len(title1_roman_wo_space)
len_title2_roman = len(title2_roman_wo_space)
if len_title1_roman < MAX_LENGTH and \
len_title2_roman < MAX_LENGTH:
spare_space1 = MAX_LENGTH - len_title1_roman
spare_space2 = MAX_LENGTH - len_title2_roman
for a in (str("-") + title1_roman_wo_space + str("_" *
(spare_space1 + 2))):
try:
word1_list.append(phone_dict[a])
except:
pass
for a in (str("-") + title2_roman_wo_space + str("_" *
(spare_space2 + 2))):
try:
word2_list.append(phone_dict[a])
except:
pass
for i in range(len(word1_list) - 1):
x_, y_ = word1_list[i], word1_list[i + 1]
i += 1
x1.append(x_)
y1.append(y_)
for i in range(len(word2_list) - 1):
x_, y_ = word2_list[i], word2_list[i + 1]
i += 1
x2.append(x_)
y2.append(y_)
plt.figure(figsize=(2.24, 2.24))
for i in range(NPOINTS1 - 1):
plt.plot(x1[i:i + 2],
y1[i:i + 2],
alpha=float(NPOINTS1 - 1 - i) / (NPOINTS1 - 1),
linewidth=5 * math.log10(1000. / len_title1_roman) *
float(NPOINTS1 - 1 - i) / (NPOINTS1 - 1),
color='black')
plt.plot(47, 47, alpha=0.00000000001)
plt.axis('off')
title1_refine = re.sub(r"[`;&<>~0-9\[\]|:+-/.,!?@#$%^*()\'\"]",
r"", title1)
filename1 = "image/" + str(title1_refine) + ".png"
plt.savefig(filename1)
img1 = cv2.imread(filename1, 0)
img1 = (img1 * -1 + 255.) / 255.
scipy.misc.imsave(filename1, img1)
# print("img1: ", img1.shape)
plt.close()
plt.figure(figsize=(2.24, 2.24))
for j in range(NPOINTS2 - 1):
plt.plot(x2[j:j + 2],
y2[j:j + 2],
alpha=float(NPOINTS2 - 1 - j) / (NPOINTS2 - 1),
linewidth=5 * math.log10(1000. / len_title2_roman) *
float(NPOINTS2 - 1 - j) / (NPOINTS2 - 1),
color='black')
plt.plot(47, 47, alpha=0.00000000001)
plt.axis('off')
title2_refine = re.sub(r"[`;&<>~0-9\[\]|:+-/.,!?@#$%^*()\'\"]",
r"", title2)
filename2 = "image/" + str(title2_refine) + ".png"
plt.savefig(filename2)
img2 = cv2.imread(filename2, 0)
img2 = (img2 * -1 + 255.) / 255.
scipy.misc.imsave(filename2, img2)
# print("img2: ", img2.shape)
plt.close()
img = np.dstack((img1, img2, img3))
return img
else:
pass
def textpair_to_array(title1, title2, similarity=0):
word_list1 = []
word_list2 = []
title1_roman = romanize(title1).lower()
title1_roman_wo_space = re.sub(
r" ", r"_-",
re.sub(r"[`;<>~0-9\[\]|:+-/.,!?@#$%^*()\'\"]", r" ",
re.sub(r"&", r" and ", title1_roman)))
title2_roman = romanize(title2).lower()
title2_roman_wo_space = re.sub(
r" ", r"_-",
re.sub(r"[`;<>~0-9\[\]|:+-/.,!?@#$%^*()\'\"]", r" ",
re.sub(r"&", r" and ", title2_roman)))
NPOINTS1 = len(title1_roman_wo_space)
NPOINTS2 = len(title2_roman_wo_space)
x1 = []
y1 = []
x2 = []
y2 = []
for a in title1_roman_wo_space:
try:
word_list1.append(phone_dict[a])
except Exception as e:
print(e)
for b in title2_roman_wo_space:
try:
word_list2.append(phone_dict[b])
except Exception as e:
print(e)
for i in range(len(word_list1) - 1):
x1_, y1_ = word_list1[i], word_list1[i + 1]
x1.append(x1_)
y1.append(y1_)
for j in range(len(word_list2) - 1):
x2_, y2_ = word_list2[j], word_list2[j + 1]
x2.append(x2_)
y2.append(y2_)
length_line1 = 0
length_line2 = 0
for i in range(NPOINTS1 - 2):
line_piece = np.linalg.norm([x1[i + 1] - x1[i], y1[i + 1] - y1[i]])
length_line1 = length_line1 + line_piece
for j in range(NPOINTS2 - 2):
line_piece = np.linalg.norm([x2[j + 1] - x2[j], y2[j + 1] - y2[j]])
length_line2 = length_line2 + line_piece
# print(length_line1, length_line2)
img = np.zeros([96, 96], dtype=np.float64)
img1 = np.zeros([96, 96], dtype=np.float64)
img2 = np.zeros([96, 96], dtype=np.float64)
# img3 = np.zeros([96, 96], dtype=np.float64)
for i in range(NPOINTS1 - 2):
img1 = cv2.line(img, (x1[i], y1[i]), (x1[i + 1], y1[i + 1]),
(255. / (i + 1)),
min(10, max(2, int(2000 / length_line1))))
for j in range(NPOINTS2 - 2):
img2 = cv2.line(img, (x2[j], y2[j]), (x2[j + 1], y2[j + 1]),
(255. / (j + 1)),
min(10, max(2, int(2000 / length_line2))))
print(length_line1, length_line2)
img1 = img1 / 255.
img2 = img2 / 255.
img = np.dstack((img1, img2))
# 직접 확인하고 싶을 경우 img3=0 을 포함하여 확인
# img = np.dstack((img1, img2, img3))
# cv2.imshow('image', img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
return img
import pandas as pd
from data.text_to_img import save_pair_image, text_to_image, textpair_to_image
from converter.romanize import romanize
import re
data = pd.read_csv('sample_trademark_data.csv')
num_data = len(data)
print(num_data)
MAX_LENGTH = 20
for i in range(num_data):
title1_roman = romanize(data['title1'][i]).lower()
title2_roman = romanize(data['title2'][i]).lower()
title1_roman_wo_space = re.sub(
r" ", r"_-",
re.sub(r"[`;<>~\[\]|:+-/.,!?@#$%^*()\'\"]", r"",
re.sub(r"&", r" and ", title1_roman)))
title2_roman_wo_space = re.sub(
r" ", r"_-",
re.sub(r"[`;<>~\[\]|:+-/.,!?@#$%^*()\'\"]", r"",
re.sub(r"&", r" and ", title2_roman)))
if len(title1_roman_wo_space) < MAX_LENGTH and len(
title2_roman_wo_space) < MAX_LENGTH:
if bool(re.search(r'\d', data['title1'][i])) == False and bool(
re.search(r'\d', data['title2'][i])) == False:
try:
save_pair_image(data['title1'][i], data['title2'][i],
data['similarity'][i])
except: