X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fbox_utils%2Ftf_ops.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fbox_utils%2Ftf_ops.py;h=0000000000000000000000000000000000000000;hb=3ed2c61d9d7e7916481650c41bfe5604f7db22e9;hp=86d945af0747842ff7460c371f7cb133f458e30e;hpb=e6d40ddb2640f434a9d7d7ed99566e5e8fa60cc1;p=ealt-edge.git

diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/tf_ops.py b/example-apps/PDD/pcb-defect-detection/libs/box_utils/tf_ops.py
deleted file mode 100755
index 86d945a..0000000
--- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/tf_ops.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# -*- 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()