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utilities.py
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utilities.py
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#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import os, cv2, csv
import numpy as np
# In[ ]:
def one_hot_encoding(text, allowedChars):
label_list = []
for c in text:
onehot = [0] * len(allowedChars)
onehot[allowedChars.index(c)] = 1
label_list.append(onehot)
return label_list
# In[ ]:
def one_hot_decoding(prediction, allowedChars):
text = ''
for predict in prediction:
value = np.argmax(predict[0])
text += allowedChars[value]
return text
# In[1]:
def read_train_data(filename, size):
train_data = []
if os.path.isdir(filename):
train_data = np.stack([np.array(cv2.imread(filename + str(index) + ".jpg"))/127.5 - 1 for index in range(1, size + 1)])
return train_data
# In[ ]:
def read_label_data(filename, allowedChars, num_dic, size):
train_label = []
traincsv = open(filename, 'r', encoding = 'utf8')
read_label = [one_hot_encoding(row[0], allowedChars) for row in csv.reader(traincsv)]
read_label = read_label[:size]
train_label = [[] for _ in range(num_dic)]
for arr in read_label:
for index in range(num_dic):
train_label[index].append(arr[index])
train_label = [arr for arr in np.asarray(train_label)]
return train_label
# In[ ]:
import matplotlib.pyplot as plt
def show_train_history(train_history, train, validation):
plt.plot(train_history.history[train])
plt.plot(train_history.history[validation])
plt.title('Train History')
plt.ylabel(train)
plt.xlabel('Epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
# In[ ]:
from keras.models import Model
from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.layers import BatchNormalization
def build_vgg_model(width, height, allowedChars, num_digit):
tensor_in = Input((height, width, 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), padding='same', activation='relu')(tensor_out)
tensor_out = MaxPooling2D(pool_size=(2, 2))(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), padding='same', activation='relu')(tensor_out)
tensor_out = MaxPooling2D(pool_size=(2, 2))(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), padding='same', activation='relu')(tensor_out)
tensor_out = BatchNormalization(axis=1)(tensor_out)
tensor_out = MaxPooling2D(pool_size=(2, 2))(tensor_out)
tensor_out = Conv2D(filters=256, kernel_size=(3, 3), padding='same', activation='relu')(tensor_out)
tensor_out = Conv2D(filters=256, kernel_size=(3, 3), padding='same', activation='relu')(tensor_out)
tensor_out = MaxPooling2D(pool_size=(2, 2))(tensor_out)
tensor_out = Conv2D(filters=512, kernel_size=(3, 3), padding='same', activation='relu')(tensor_out)
tensor_out = BatchNormalization(axis=1)(tensor_out)
tensor_out = MaxPooling2D(pool_size=(2, 2))(tensor_out)
tensor_out = Flatten()(tensor_out)
tensor_out = Dropout(0.5)(tensor_out)
tensor_out = [Dense(len(allowedChars), name='digit' + str(i), activation='softmax')(tensor_out) for i in range(1, num_digit + 1)]
model = Model(inputs=tensor_in, outputs=tensor_out)
model.compile(loss='categorical_crossentropy', optimizer='Adamax', metrics=['accuracy'])
model.summary()
return model
# In[ ]:
from keras.applications import ResNet50
def build_resnet50_model(size, allowedChars, num_digit):
model = ResNet50(weights='imagenet', include_top=False, input_shape=(size, size, 3))
tensor_in = model.input
tensor_out = model.output
tensor_out = Flatten()(tensor_out)
tensor_out = Dropout(0.5)(tensor_out)
outputs = [Dense(len(allowedChars), name='digit' + str(i), activation='softmax')(tensor_out) for i in range(1, num_digit + 1)]
model2 = Model(tensor_in, outputs)
model2.compile(loss='categorical_crossentropy', optimizer='Adamax', metrics=['accuracy'])
model2.summary()
return model2
# In[ ]:
from keras.applications import InceptionV3
def build_inceptionv3_model(size, allowedChars, num_digit):
model = InceptionV3(weights='imagenet', include_top=False, input_shape=(size, size, 3))
tensor_in = model.input
tensor_out = model.output
tensor_out = Flatten()(tensor_out)
tensor_out = Dropout(0.5)(tensor_out)
outputs = [Dense(len(allowedChars), name='digit' + str(i), activation='softmax')(tensor_out) for i in range(1, num_digit + 1)]
model2 = Model(tensor_in, outputs)
model2.compile(loss='categorical_crossentropy', optimizer='Adamax', metrics=['accuracy'])
model2.summary()
return model2