import numpy as np
import matplotlib.pyplot as plt
try:
    from scipy import misc
except ImportError:
    !pip install scipy
    from scipy import misc

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


training_size = 265
col_row  = 400
img_size = 400*400
training_data = np.empty(shape=(training_size, 400*400))

import glob
i = 0
for filename in glob.glob('C:/Mah/TensorPalms/Train/*.jpg'):
    image = imageio.imread(filename)
    print(image.shape)
    training_data[i] = image.reshape(-1)
    i+=1


#label


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



b = tf.one_hot(a,53)
sess = tf.Session()
sess.run(b)

print(b.shape)

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



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

test_size = 159
img_size = 400*400

test_images = np.empty(shape=(test_size,400*400))
import glob
i = 0
for filename in glob.glob('C:/Mah/TensorPalms/Test/*.jpg'):
    image = imageio.imread(filename)
    print(image.shape)
    test_images[i] = image.reshape(-1)
    i+=1


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


test_labels = tf.one_hot(c,53)
sess = tf.Session()
sess.run(test_labels)




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


import tensorflow as tf
sess = tf.InteractiveSession()




def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)

def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)



def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')



# Input layer

x = tf.placeholder(tf.float32, [None, img_size])
y_ = tf.placeholder(tf.float32, [None, 53])
x_image = tf.reshape(x, [-1, 400, 400, 1])

#First Convolutional Layer,Convolutional layer 1


W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1, 400, 400, 1])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

#Second Convolutional Layer,Convolutional layer 2

W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)


#Densely Connected Layer

W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024 ])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)


#Dropout

keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

#Readout Layer,Fully connected layer  (Output layer)

W_fc2 = weight_variable([1024, 53])
b_fc2 = bias_variable([53])

y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2



import numpy as np

def next_batch(num, data, labels):
    '''
    Return a total of `num` random samples and labels.
    '''
    idx = np.arange(0 , len(data))
    np.random.shuffle(idx)
    idx = idx[:num]
    data_shuffle = [data[ i] for i in idx]
    labels_shuffle = [labels[ i] for i in idx]

    return np.asarray(data_shuffle), np.asarray(labels_shuffle)




cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  for i in range(20000):
    batch_xs, batch_ys = next_batch(50,training_data,training_label)
    array_batch_xs = np.reshape(batch_xs, [-1, img_size ])

    if i % 100 == 0:
      train_accuracy = accuracy.eval(feed_dict={
          x: array_batch_xs, y_: batch_ys, keep_prob: 1.0})
      print('step %d, training accuracy %g' % (i, train_accuracy))
    train_step.run(feed_dict={x: array_batch_xs, y_: batch_ys, keep_prob: 0.5})

  print('test accuracy %g' % accuracy.eval(feed_dict={
      x: test_images, y_: test_label, keep_prob: 1.0}))