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


  ############################################ train
training_size = 300
img_size = 20*20*3

#class Struct:
#    "A structure that can have any fields defined."
 #   def __init__(self, **entries): self.__dict__.update(entries)
#training_images=[];
#training_labels=[];
#training_data= Struct(training_set=training_images, labels=training_labels)
training_images = np.empty(shape=(training_size,20,20,3))
import glob
i = 0
for filename in glob.glob('D:/Minutia/PrincipleWrinkleMinutia/*.jpg'):
    image = misc.imread(filename)
    training_images[i] = image
    i+=1
print(training_images[0].shape)

a= [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
    2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2]
training_labels = tf.one_hot(a,3)
sess = tf.Session()
sess.run(training_labels)


#################################################### test
test_size = 300
img_size = 20*20*3
#class Struct:

#test_images=[];
#test_labels=[];
#test_data= Struct(training_set=test_images, labels=test_labels)

test_images = np.empty(shape=(test_size,20,20,3))
import glob
i = 0
for filename in glob.glob('D:/Minutia/PrincipleWrinkleMinutia/*.jpg'):
    image = misc.imread(filename)
    test_images[i] = image
    i+=1
print(test_images[0].shape)
a= [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
    2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2]
test_labels = tf.one_hot(a,3)
sess = tf.Session()
sess.run(test_labels)


x = tf.placeholder(tf.float64, [None, img_size])
W = tf.Variable(tf.zeros([img_size, 3], dtype=tf.float64))
b = tf.Variable(tf.zeros([3], dtype=tf.float64))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float64, [None, 3])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()


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)

#Xtr, Ytr = np.arange(0, 10), np.arange(0, 100).reshape(10, 10)

#print(Xtr)
#print(Ytr)

#Xtr, Ytr = next_batch(5, Xtr, Ytr)
#print('\n5 random samples')
#print(Xtr)
#print(Ytr)

for _ in range(1000):
 batch_size=100

 batch_xs, batch_ys = next_batch(100,training_images,training_labels)


array_batch_xs= np.reshape(batch_xs, [-1, img_size ])


sess.run(train_step, feed_dict={x: array_batch_xs, y: batch_ys})
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
correct_prediction = tf.equal(training_images, training_labels)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))
print(sess.run(accuracy, feed_dict={x: test_images, y_: test_labels}))