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build_verification_code_cnn_model.py
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/
build_verification_code_cnn_model.py
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import cv2
import numpy as np
import os, sys
import matplotlib.pyplot as plt
#-------------------------- set gpu using tf ---------------------------
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
#------------------- start importing keras module ---------------------
from keras.models import Model
from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.optimizers import RMSprop
import platform
if 'Windows' in platform.platform():
PATH = "\\".join( os.path.abspath(__file__).split('\\')[:])
else:
PATH = "/".join( os.path.abspath(__file__).split('/')[:])
sys.path.append(PATH)
sys.path.append(PATH)
#=====================================================================
# self = build_verification_code_cnn_model()
class build_verification_code_cnn_model:
def __init__(self):
total_set = []
for i in range(65, 91):
total_set.append( chr(i) )
for i in range(10):
total_set.append(str(i))
total_set.append('null')
self.total_set = total_set
def build_model_process(self):
self.train_data,self.train_labels = self.input_data('train_data',200000)
self.test_data,self.test_labels = self.input_data('test_data',40000)
self.train_verification_model()
print( self.train_correct3,'\n' , self.test_correct3 )
print( self.train_final_score,'\n', self.test_final_score )
self.show_train_history()
os.chdir(PATH)
if 'cnn_weight' not in os.listdir():
os.makedirs('cnn_weight')
if 'Windows' in platform.platform():
self.model.save_weights('{}\\cnn_weight\\verificatioin_code.h5'.format(PATH))
else:
self.model.save_weights('{}/cnn_weight/verificatioin_code.h5'.format(PATH))
#===============================================================
def input_data(self,file_path,n):
if 'Windows' in platform.platform():
file_path = '\\{}\\{}\\'.format(PATH,file_path)
train_image_path = [file_path + i for i in os.listdir(file_path+'\\')][:n]
else:
file_path = '/{}/{}/'.format(PATH,file_path)
train_image_path = [file_path + i for i in os.listdir(file_path+'/')][:n]
#-------------------------------------------------------
def change_onehotencoder(text_set,total_set):
total_amount = len(total_set)
labellist = []
for number in text_set:
#print('number : '+str(number))
onehot = [0 for _ in range(total_amount)]
for i in range(len(total_set)):
if number == total_set[i]:
onehot[i] = 1
labellist.append(onehot)
break
if len(labellist) == 5:
onehot = [0 for _ in range(total_amount)]
#print('null')
onehot[len(total_set)-1] = 1
labellist.append(onehot)
return labellist
#-------------------------------------------------------
# label to ont hot encoder
# Y要是一個含有6個numpy array的list
labels_set = [[] for _ in range(6)]
for text in train_image_path:
text = text.replace(file_path,'')
text_set = text.replace('.jpg','')
text_set = change_onehotencoder(text_set,self.total_set)
for i in range(6):
labels_set[i].append(text_set[i])
labels_set = [arr for arr in np.asarray(labels_set)]# 最後要把6個numpy array 放在一個list
#-------------------------------------------------------
# input image to array
sum_count = len(train_image_path)
train_set = np.ndarray(( sum_count , 60, 200,3), dtype=np.uint8)
i=0
while( i < sum_count ):
image_name = train_image_path[i]
image = cv2.imread(image_name)
#plt.imshow(im)
train_set[i] = image
i=i+1
if i%50 == 0: print('Processed {} of {}'.format(i, sum_count ) )
return train_set,labels_set
#===============================================================
def train_verification_model(self):
def build_cnn_model():
tensor_in = Input((60, 200, 3))
tensor_out = tensor_in
tensor_out = Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(tensor_out)
tensor_out = Conv2D(filters=32, kernel_size=(3, 3), activation='relu')(tensor_out)
tensor_out = MaxPooling2D(pool_size=(2, 2))(tensor_out)
tensor_out = Dropout(0.25)(tensor_out)
tensor_out = Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu')(tensor_out)
tensor_out = Conv2D(filters=64, kernel_size=(3, 3), activation='relu')(tensor_out)
tensor_out = MaxPooling2D(pool_size=(2, 2))(tensor_out)
tensor_out = Dropout(0.25)(tensor_out)
tensor_out = Conv2D(filters=128, kernel_size=(3, 3), padding='same', activation='relu')(tensor_out)
tensor_out = Conv2D(filters=128, kernel_size=(3, 3), activation='relu')(tensor_out)
tensor_out = MaxPooling2D(pool_size=(2, 2))(tensor_out)
tensor_out = Dropout(0.25)(tensor_out)
tensor_out = Conv2D(filters=256, kernel_size=(3, 3), activation='relu')(tensor_out)
tensor_out = MaxPooling2D(pool_size=(2, 2))(tensor_out)
Dense(1024, activation = "relu")
tensor_out = Flatten()(tensor_out)
tensor_out = Dropout(0.5)(tensor_out)
tensor_out = [Dense(37, name='digit1', activation='softmax')(tensor_out),\
Dense(37, name='digit2', activation='softmax')(tensor_out),\
Dense(37, name='digit3', activation='softmax')(tensor_out),\
Dense(37, name='digit4', activation='softmax')(tensor_out),\
Dense(37, name='digit5', activation='softmax')(tensor_out),\
Dense(37, name='digit6', activation='softmax')(tensor_out)]
model = Model(inputs=tensor_in, outputs=tensor_out)
return model
model = build_cnn_model()
#===============================================================
optimizer = RMSprop(lr=1e-3, rho=0.8, epsilon=1e-08, decay=0.0)
# Adamax
# Define the optimizer
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
# model.summary()
history = model.fit(self.train_data,self.train_labels,
batch_size = 512, epochs=20, verbose=1,
validation_data=(self.test_data,self.test_labels) )
self.model = model
self.history = history
( self.train_correct3 , self.test_correct3,
self.train_final_score, self.test_final_score ) = self.compare_val_train_error()
#-------------------------------------------------------------------
def compare_val_train_error(self):
#-----------------------------------------------
def change_character(pred_prob,total_set):
#total_amount = len(total_set)
for i in range(len(pred_prob)):
if pred_prob[i] == max( pred_prob ):
value = (total_set[i])
if value == 'null':
return ''
return value
#-----------------------------------------------
def compare_error(file_path,self,data):
if 'Windows' in platform.platform():
file_path = '\\{}\\{}\\'.format(PATH,file_path)
train_image_path = [file_path + i for i in os.listdir(file_path+'\\')][:len(data)]
else:
file_path = '/{}/{}/'.format(PATH,file_path)
train_image_path = [file_path + i for i in os.listdir(file_path+'/')][:len(data)]
labels_set=[]
for text in train_image_path:
text = text.replace(file_path,'')
text = text.replace('.jpg','')
labels_set.append( text )
#----------------------------------------------------------------
#prediction = self.model.predict(self.train_data, verbose=1)
prediction = self.model.predict(data, verbose=1)
amount = len(labels_set)
resultlist = ["" for _ in range(amount)]
for i in range(amount):
for j in range(len(prediction)):
#print(j)
resultlist[i] += change_character(prediction[j][i],self.total_set)
#resultlist[:10]
#----------------------------------------------------------------
total = len(resultlist)
score = [0 for _ in range(6)]
for i in range(total):
#print(i)
for j in range(6):
#print(resultlist[i][j])
try:
if resultlist[i][j] == labels_set[i][j]:
score[j] += 1
except:
123
for i in range(6):
score[i] /= total
#current_per = score/total
return score
#----------------------------------------------------------------
#-----------------------------------------------
def compare_final_error(file_path,self,data):
# file_path = 'test_data'
# data = self.train_data
if 'Windows' in platform.platform():
file_path = '\\{}\\{}\\'.format(PATH,file_path)
train_image_path = [file_path + i for i in os.listdir(file_path+'\\')][:len(data)]
else:
file_path = '/{}/{}/'.format(PATH,file_path)
train_image_path = [file_path + i for i in os.listdir(file_path+'/')][:len(data)]
labels_set=[]
for text in train_image_path:
text = text.replace(file_path,'')
text = text.replace('.jpg','')
labels_set.append( text )
#----------------------------------------------------------------
prediction = self.model.predict(data, verbose=1)
amount = len(labels_set)
resultlist = ["" for _ in range(amount)]
for i in range(amount):
for j in range(len(prediction)):
#print(j)
resultlist[i] += change_character(prediction[j][i],self.total_set)
#----------------------------------------------------------------
total = len(resultlist)
score = 0
for i in range(total):
if resultlist[i] == labels_set[i]: score = score+1
score = score/total
return score
#----------------------------------------------------------------
v1 = compare_error('train_data',self,self.train_data)
v2 = compare_error('test_data',self,self.test_data)
v3 = 0#compare_final_error('train_data',self,self.train_data)
v4 = compare_final_error('test_data',self,self.test_data)
return v1,v2,v3,v4
#===============================================================
def show_train_history(self):#(train = 'acc', validation = 'val_acc'):
plt.figure(figsize = (10,10)) # change figure size
for i in range(1,7,1):
#print(i)
plt.plot( self.history.history['digit'+str(i)+'_acc'] )
plt.plot( self.history.history['val_digit'+str(i)+'_acc'] )
plt.title('train history')
plt.ylabel('acc'+str(i))
plt.xlabel('Epoch'+str(i))
plt.legend(['digit1_acc','val_digit1_acc',
'digit2_acc','val_digit2_acc',
'digit3_acc','val_digit3_acc',
'digit4_acc','val_digit4_acc',
'digit5_acc','val_digit5_acc',
'digit6_acc','val_digit6_acc'],loc = 'upper left')
#===============================================================
#===============================================================
def main():
import work_vcode
#import time
work_vcode.work_vcode_fun(500,'train_data',5)
work_vcode.work_vcode_fun(500,'train_data',6)
work_vcode.work_vcode_fun(10,'test_data',5)
work_vcode.work_vcode_fun(10,'test_data',6)
self = build_verification_code_cnn_model()
self.build_model_process()