-
Notifications
You must be signed in to change notification settings - Fork 0
/
recognizer.py
154 lines (120 loc) · 5.09 KB
/
recognizer.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
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
#coding=utf-8
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D,MaxPool2D
from keras.optimizers import SGD
from keras import backend as K
K.set_image_data_format("channels_last")
import cv2
import numpy as np
index = {"京": 0, "沪": 1, "津": 2, "渝": 3, "冀": 4, "晋": 5, "蒙": 6, "辽": 7, "吉": 8, "黑": 9, "苏": 10, "浙": 11, "皖": 12,
"闽": 13, "赣": 14, "鲁": 15, "豫": 16, "鄂": 17, "湘": 18, "粤": 19, "桂": 20, "琼": 21, "川": 22, "贵": 23, "云": 24,
"藏": 25, "陕": 26, "甘": 27, "青": 28, "宁": 29, "新": 30, "0": 31, "1": 32, "2": 33, "3": 34, "4": 35, "5": 36,
"6": 37, "7": 38, "8": 39, "9": 40, "A": 41, "B": 42, "C": 43, "D": 44, "E": 45, "F": 46, "G": 47, "H": 48,
"J": 49, "K": 50, "L": 51, "M": 52, "N": 53, "P": 54, "Q": 55, "R": 56, "S": 57, "T": 58, "U": 59, "V": 60,
"W": 61, "X": 62, "Y": 63, "Z": 64,"港":65,"学":66 ,"O":67 ,"使":68,"警":69,"澳":70,"挂":71};
chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
"琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
"B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P",
"Q", "R", "S", "T", "U", "V", "W", "X",
"Y", "Z","港","学","O","使","警","澳","挂" ];
def Getmodel_tensorflow(nb_classes):
# nb_classes = len(charset)
img_rows, img_cols = 23, 23
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
nb_pool = 2
# convolution kernel size
nb_conv = 3
# x = np.load('x.npy')
# y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
# weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
# weight = dict(zip(range(3063), weight / weight.mean())) # 调整权重，高频字优先
model = Sequential()
model.add(Conv2D(32, (5, 5),input_shape=(img_rows, img_cols,1)))
model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
model.add(Dropout(0.25))
model.add(Conv2D(512, (3, 3)))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
# model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
def Getmodel_ch(nb_classes):
# nb_classes = len(charset)
img_rows, img_cols = 23, 23
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
nb_pool = 2
# convolution kernel size
nb_conv = 3
# x = np.load('x.npy')
# y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
# weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
# weight = dict(zip(range(3063), weight / weight.mean())) # 调整权重，高频字优先
model = Sequential()
model.add(Conv2D(32, (5, 5),input_shape=(img_rows, img_cols,1)))
model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
model.add(Dropout(0.25))
model.add(Conv2D(512, (3, 3)))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
# model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(756))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
model = Getmodel_tensorflow(65)
#构建网络
model_ch = Getmodel_ch(31)
model_ch.load_weights("./model/char_chi_sim.h5")
# model_ch.save_weights("./model/char_chi_sim.h5")
model.load_weights("./model/char_rec.h5")
# model.save("./model/char_rec.h5")
def SimplePredict(image,pos):
image = cv2.resize(image, (23, 23))
image = cv2.equalizeHist(image)
image = image.astype(np.float) / 255
image -= image.mean()
image = np.expand_dims(image, 3)
if pos!=0:
res = np.array(model.predict(np.array([image]))[0])
else:
res = np.array(model_ch.predict(np.array([image]))[0])
zero_add = 0 ;
if pos==0:
res = res[:31]
elif pos==1:
res = res[31+10:65]
zero_add = 31+10
else:
res = res[31:]
zero_add = 31
max_id = res.argmax()
return res.max(),chars[max_id+zero_add],max_id+zero_add