# -*- coding:utf-8 -*-

from __future__ import absolute_import, print_function, division

import tensorflow as tf

'''
all of these ops are derived from tenosrflow Object Detection API
'''
def indices_to_dense_vector(indices,
                            size,
                            indices_value=1.,
                            default_value=0,
                            dtype=tf.float32):
  """Creates dense vector with indices set to specific (the para "indices_value" ) and rest to zeros.

  This function exists because it is unclear if it is safe to use
    tf.sparse_to_dense(indices, [size], 1, validate_indices=False)
  with indices which are not ordered.
  This function accepts a dynamic size (e.g. tf.shape(tensor)[0])

  Args:
    indices: 1d Tensor with integer indices which are to be set to
        indices_values.
    size: scalar with size (integer) of output Tensor.
    indices_value: values of elements specified by indices in the output vector
    default_value: values of other elements in the output vector.
    dtype: data type.

  Returns:
    dense 1D Tensor of shape [size] with indices set to indices_values and the
        rest set to default_value.
  """
  size = tf.to_int32(size)
  zeros = tf.ones([size], dtype=dtype) * default_value
  values = tf.ones_like(indices, dtype=dtype) * indices_value

  return tf.dynamic_stitch([tf.range(size), tf.to_int32(indices)],
                           [zeros, values])




def test_plt():
  from PIL import Image
  import matplotlib.pyplot as plt
  import numpy as np

  a = np.random.rand(20, 30)
  print (a.shape)
  # plt.subplot()
  b = plt.imshow(a)
  plt.show()


if __name__ == '__main__':
  test_plt()