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


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:
 #   "A structure that can have any fields defined."
  #  def __init__(self, **entries): self.__dict__.update(entries)
#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)

    #################################################### test
test_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)
#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)
 #Similar to  MNIST
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()
tf.local_variables_initializer().run()

def next_batch(self, batch_size, fake_data=False, shuffle=True):
    """Return the next `batch_size` examples from this data set."""
    if fake_data:
      fake_image = [1] * 784
      if self.one_hot:
        fake_label = [1] + [0] * 9
      else:
        fake_label = 0
      return [fake_image for _ in xrange(batch_size)], [
          fake_label for _ in xrange(batch_size)
      ]
    start = self._index_in_epoch
    # Shuffle for the first epoch
    if self._epochs_completed == 0 and start == 0 and shuffle:
      perm0 = numpy.arange(self._num_examples)
      numpy.random.shuffle(perm0)
      self._images = self.images[perm0]
      self._labels = self.labels[perm0]
    # Go to the next epoch
    if start + batch_size > self._num_examples:
      # Finished epoch
      self._epochs_completed += 1
      # Get the rest examples in this epoch
      rest_num_examples = self._num_examples - start
      images_rest_part = self._images[start:self._num_examples]
      labels_rest_part = self._labels[start:self._num_examples]
      # Shuffle the data
      if shuffle:
        perm = numpy.arange(self._num_examples)
        numpy.random.shuffle(perm)
        self._images = self.images[perm]
        self._labels = self.labels[perm]
      # Start next epoch
      start = 0
      self._index_in_epoch = batch_size - rest_num_examples
      end = self._index_in_epoch
      images_new_part = self._images[start:end]
      labels_new_part = self._labels[start:end]
      return numpy.concatenate((images_rest_part, images_new_part), axis=0) , numpy.concatenate((labels_rest_part, labels_new_part), axis=0)
    else:
      self._index_in_epoch += batch_size
      end = self._index_in_epoch
      return self._images[start:end], self._labels[start:end]

########
def next_batch(self):
    """The `NextQueuedSequenceBatch` providing access to batched output data.
    Also provides access to the `state` and `save_state` methods.
    The first time this gets called, it additionally prepares barrier reads
    and creates `NextQueuedSequenceBatch` / next_batch objects. Subsequent
    calls simply return the previously created `next_batch`.
    In order to access data in `next_batch` without blocking, the `prefetch_op`
    must have been run at least `batch_size` times (ideally in a separate
    thread, or launched via a `QueueRunner`). After processing a segment in
    `next_batch()`, `batch.save_state()` must be called which is done by the
    state_saving_rnn. Without this call, the dequeue op associated with the SQSS
    will not run.
    Returns:
      A cached `NextQueuedSequenceBatch` instance.
    """
    # This is needed to prevent errors if next_batch is called before
    # prefetch_op is created.
    if not self._prefetch_op:
      with ops.name_scope(None), ops.name_scope(
          self._scope, values=[self._barrier.barrier_ref]):
        self._create_prefetch_op()
    if not self._next_batch:
      with ops.name_scope(None), ops.name_scope(
          self._scope, values=[self._barrier.barrier_ref]):
        self._prepare_barrier_reads()
    return self._next_batch


for _ in range(1000):
 #batch_xs, batch_ys = training_data.next_batch(100)
 batch_xs, batch_ys = training_images.next_batch(100)
sess.run(train_step, feed_dict={x: 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}))