-
Notifications
You must be signed in to change notification settings - Fork 82
/
test_type4.py
118 lines (107 loc) · 3.99 KB
/
test_type4.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
# coding:utf-8
# __author__ = 'lllcho'
# __date__ = '2015/8/4'
import cv2
import cPickle
import numpy as np
import codecs
import h5py
import theano
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
def word_simialr_score(s1, s2):
score = 0
for j in range(len(s1)):
if s1[j] == s2[j]:
score += 1
return score
def words_simmilar_score(word, words):
word_score = {}
for Word in words:
ws = word_simialr_score(word, Word)
if ws not in word_score.keys():
word_score[ws] = [Word]
else:
word_score[ws].append(Word)
return word_score
np.random.seed(123)
model_path = './model/type4_model.d5'
chars = cPickle.load(open('model/chars_type4.pkl', 'rb'))
words = cPickle.load(open('model/words_type4.pkl', 'rb'))
chars.append('A')
f = h5py.File('./model/type4_train_mean_std.h5', 'r')
x_mean = f['x_mean'][:]
x_std = f['x_std'][:][0]
f.close()
model = Sequential()
model.add(Convolution2D(32, 3, 4, 4, border_mode='full', activation='relu'))
model.add(Convolution2D(32, 32, 4, 4, activation='relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(64, 32, 4, 4, border_mode='full', activation='relu'))
model.add(Convolution2D(64, 64, 4, 4, activation='relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(64 * 8 * 8, 512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(512, 1250, activation='softmax'))
model.load_weights(model_path)
model.compile(loss='categorical_crossentropy', optimizer='adagrad')
get_predict_score = theano.function([model.layers[0].input],
model.layers[-1].get_output(train=False),
allow_input_downcast=True)
comp = 'type4_test1'
img_dir = './image/' + comp + '/'
fcsv = codecs.open("result/" + comp + '.csv', 'w', 'utf-8')
# for nb_img in range(1, 20001):
# name=comp+'_'+str(nb_img)+'.png'
import os
names = os.listdir(img_dir)
for name in names:
print name
imgname = img_dir + name
img = cv2.imread(imgname, cv2.IMREAD_COLOR)
im = 255 - img[4:-4, :, :]
t = im.shape[1] / 4.0
dd = 5
bb = np.zeros((im.shape[0], dd, 3), dtype=np.uint8)
im1 = im[:, 0:np.floor(t) + dd]
im2 = im[:, np.floor(t) - dd:np.floor(2 * t) + dd]
im3 = im[:, np.floor(2 * t) - dd:np.floor(3 * t) + dd]
im4 = im[:, np.floor(3 * t) - dd:]
imgs = np.zeros((4, 3, 32, 32))
imgs[0, :] = cv2.resize(np.concatenate((bb, im1), axis=1), (32, 32)).transpose()
imgs[1, :] = cv2.resize(im2, (32, 32)).transpose()
imgs[2, :] = cv2.resize(im3, (32, 32)).transpose()
imgs[3, :] = cv2.resize(np.concatenate((im4, bb), axis=1), (32, 32)).transpose()
imgs.astype(np.float32)
imgs = imgs - x_mean
imgs = imgs / x_std
classes = model.predict_classes(imgs, verbose=0)
model_predict_score = get_predict_score(imgs)
result = []
for c in classes:
result.append(chars[c])
word = ''.join(result)
old_word = word
if word not in words:
word_score = words_simmilar_score(word, words)
max_score = max(word_score.keys())
if max_score > 0:
candidate_words = word_score[max_score]
predict_similar_score = {}
for candidate_word in candidate_words:
diff_chars = {}
for j in range(len(candidate_word)):
if old_word[j] != candidate_word[j]:
diff_chars[j] = candidate_word[j]
diff_chars_similar_score = 0
for key, iterm in diff_chars.items():
diff_chars_similar_score += model_predict_score[key, chars.index(iterm)]
predict_similar_score[candidate_word] = diff_chars_similar_score
word = max(predict_similar_score.items(), key=lambda x: x[1])[0]
print word
fcsv.write(name + ',' + word + '\n')
fcsv.close()