import numpy as np
from matplotlib import pyplot as plt

from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import Adam

from keras.callbacks import EarlyStopping



%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from sklearn.metrics import confusion_matrix
import time
from datetime import timedelta
import math



#Load Data
import tensorflow as tf
import numpy as np
import imageio
import matplotlib.pyplot as plt
try:
    from scipy import misc
except ImportError:
    !pip install scipy
    from scipy import misc






import tensorflow as tf
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np


training_size = 720

train_images = np.empty(shape=(training_size, 250,250,1))

import glob
i = 0
for filename in glob.glob('C:/Mah/Database/HKPU/HKPU60CLS/Train/*.bmp'):
    image = np.array(Image.open(filename))
    image1 = image[:,:, np.newaxis]
    image2 = tf.Session().run(tf.image.resize_image_with_crop_or_pad(image1, 250, 250))

    train_images[i] = image2
    i+=1


X_train  = train_images[:,:,:,0]

a= [0,0,0,0,0,0,0,0,0,0,0,0,
    1,1,1,1,1,1,1,1,1,1,1,1,
    2,2,2,2,2,2,2,2,2,2,2,2,
    3,3,3,3,3,3,3,3,3,3,3,3,
    4,4,4,4,4,4,4,4,4,4,4,4,
    5,5,5,5,5,5,5,5,5,5,5,5,
    6,6,6,6,6,6,6,6,6,6,6,6,
    7,7,7,7,7,7,7,7,7,7,7,7,
    8,8,8,8,8,8,8,8,8,8,8,8,
    9,9,9,9,9,9,9,9,9,9,9,9,
    10,10,10,10,10,10,10,10,10,10,10,10,
    11,11,11,11,11,11,11,11,11,11,11,11,
    12,12,12,12,12,12,12,12,12,12,12,12,
    13,13,13,13,13,13,13,13,13,13,13,13,
    14,14,14,14,14,14,14,14,14,14,14,14,
    15,15,15,15,15,15,15,15,15,15,15,15,
    16,16,16,16,16,16,16,16,16,16,16,16,
    17,17,17,17,17,17,17,17,17,17,17,17,
    18,18,18,18,18,18,18,18,18,18,18,18,
    19,19,19,19,19,19,19,19,19,19,19,19,
    20,20,20,20,20,20,20,20,20,20,20,20,
    21,21,21,21,21,21,21,21,21,21,21,21,
    22,22,22,22,22,22,22,22,22,22,22,22,
    23,23,23,23,23,23,23,23,23,23,23,23,
    24,24,24,24,24,24,24,24,24,24,24,24,
    25,25,25,25,25,25,25,25,25,25,25,25,
    26,26,26,26,26,26,26,26,26,26,26,26,
    27,27,27,27,27,27,27,27,27,27,27,27,
    28,28,28,28,28,28,28,28,28,28,28,28,
    29,29,29,29,29,29,29,29,29,29,29,29,
    30,30,30,30,30,30,30,30,30,30,30,30,
    31,31,31,31,31,31,31,31,31,31,31,31,
    32,32,32,32,32,32,32,32,32,32,32,32,
    33,33,33,33,33,33,33,33,33,33,33,33,
    34,34,34,34,34,34,34,34,34,34,34,34,
    35,35,35,35,35,35,35,35,35,35,35,35,
    36,36,36,36,36,36,36,36,36,36,36,36,
    37,37,37,37,37,37,37,37,37,37,37,37,
    38,38,38,38,38,38,38,38,38,38,38,38,
    39,39,39,39,39,39,39,39,39,39,39,39,
    40,40,40,40,40,40,40,40,40,40,40,40,
    41,41,41,41,41,41,41,41,41,41,41,41,
    42,42,42,42,42,42,42,42,42,42,42,42,
    43,43,43,43,43,43,43,43,43,43,43,43,
    44,44,44,44,44,44,44,44,44,44,44,44,
    45,45,45,45,45,45,45,45,45,45,45,45,
    46,46,46,46,46,46,46,46,46,46,46,46,
    47,47,47,47,47,47,47,47,47,47,47,47,
    48,48,48,48,48,48,48,48,48,48,48,48,
    49,49,49,49,49,49,49,49,49,49,49,49,
    50,50,50,50,50,50,50,50,50,50,50,50,
    51,51,51,51,51,51,51,51,51,51,51,51,
    52,52,52,52,52,52,52,52,52,52,52,52,
    53,53,53,53,53,53,53,53,53,53,53,53,
    54,54,54,54,54,54,54,54,54,54,54,54,
    55,55,55,55,55,55,55,55,55,55,55,55,
    56,56,56,56,56,56,56,56,56,56,56,56,
    57,57,57,57,57,57,57,57,57,57,57,57,
    58,58,58,58,58,58,58,58,58,58,58,58,
    59,59,59,59,59,59,59,59,59,59,59,59]



from sklearn.preprocessing import OneHotEncoder
train_labels = OneHotEncoder(sparse=False).fit_transform(np.asarray(a).reshape(-1, 1))
print(train_labels)


y_train = np.argmax(train_labels, axis=1)







print(X_train.shape)
print(y_train.shape)







#Load Data
import tensorflow as tf
import numpy as np
import imageio
import matplotlib.pyplot as plt
try:
    from scipy import misc
except ImportError:
    !pip install scipy
    from scipy import misc











test_size = 480
img_size = 384*284
test_images = np.empty(shape=(test_size, 250,250,1))

import glob
i = 0
for filename in glob.glob('C:/Mah/Database/HKPU/HKPU60CLS/Test/*.bmp'):
    image = np.array(Image.open(filename))
    image1 = image[:,:, np.newaxis]
    image2 = tf.Session().run(tf.image.resize_image_with_crop_or_pad(image1, 250, 250))
    test_images[i] = image2

    i+=1

X_test  = test_images[:,:,:,0]


c= [0,0,0,0,0,0,0,0,
    1,1,1,1,1,1,1,1,
    2,2,2,2,2,2,2,2,
    3,3,3,3,3,3,3,3,
    4,4,4,4,4,4,4,4,
    5,5,5,5,5,5,5,5,
    6,6,6,6,6,6,6,6,
    7,7,7,7,7,7,7,7,
    8,8,8,8,8,8,8,8,
    9,9,9,9,9,9,9,9,
    10,10,10,10,10,10,10,10,
    11,11,11,11,11,11,11,11,
    12,12,12,12,12,12,12,12,
    13,13,13,13,13,13,13,13,
    14,14,14,14,14,14,14,14,
    15,15,15,15,15,15,15,15,
    16,16,16,16,16,16,16,16,
    17,17,17,17,17,17,17,17,
    18,18,18,18,18,18,18,18,
    19,19,19,19,19,19,19,19,
    20,20,20,20,20,20,20,20,
    21,21,21,21,21,21,21,21,
    22,22,22,22,22,22,22,22,
    23,23,23,23,23,23,23,23,
    24,24,24,24,24,24,24,24,
    25,25,25,25,25,25,25,25,
    26,26,26,26,26,26,26,26,
    27,27,27,27,27,27,27,27,
    28,28,28,28,28,28,28,28,
    29,29,29,29,29,29,29,29,
    30,30,30,30,30,30,30,30,
    31,31,31,31,31,31,31,31,
    32,32,32,32,32,32,32,32,
    33,33,33,33,33,33,33,33,
    34,34,34,34,34,34,34,34,
    35,35,35,35,35,35,35,35,
    36,36,36,36,36,36,36,36,
    37,37,37,37,37,37,37,37,
    38,38,38,38,38,38,38,38,
    39,39,39,39,39,39,39,39,
    40,40,40,40,40,40,40,40,
    41,41,41,41,41,41,41,41,
    42,42,42,42,42,42,42,42,
    43,43,43,43,43,43,43,43,
    44,44,44,44,44,44,44,44,
    45,45,45,45,45,45,45,45,
    46,46,46,46,46,46,46,46,
    47,47,47,47,47,47,47,47,
    48,48,48,48,48,48,48,48,
    49,49,49,49,49,49,49,49,
    50,50,50,50,50,50,50,50,
    51,51,51,51,51,51,51,51,
    52,52,52,52,52,52,52,52,
    53,53,53,53,53,53,53,53,
    54,54,54,54,54,54,54,54,
    55,55,55,55,55,55,55,55,
    56,56,56,56,56,56,56,56,
    57,57,57,57,57,57,57,57,
    58,58,58,58,58,58,58,58,
    59,59,59,59,59,59,59,59]



from sklearn.preprocessing import OneHotEncoder
test_labels = OneHotEncoder(sparse=False).fit_transform(np.asarray(c).reshape(-1, 1))
print(test_labels)


y_test = np.argmax(test_labels, axis=1)


print(y_train.shape)
print(X_test.shape)
print(y_test.shape)






img_rows, img_cols = X_train[0].shape[0], X_train[0].shape[1]
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)





X_train = X_train.astype('float32')/255.
X_test = X_test.astype('float32')/255.







n_classes = len(set(y_train))
y_train = np_utils.to_categorical(y_train, n_classes)
y_test = np_utils.to_categorical(y_test, n_classes)



model = Sequential()

model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_classes, activation='softmax'))

model.compile(loss=['categorical_crossentropy'], optimizer='adam', metrics=['accuracy'])

model.summary()


callbacks = [EarlyStopping(monitor='val_acc', patience=5)]


batch_size = 50
n_epochs = 1000






model.fit(X_train, y_train, batch_size=batch_size, epochs=n_epochs, verbose=1, validation_split=0.2, callbacks=callbacks)





score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])