def _create_operations(self, examples_per_record):
"""
Create the operations to read images from the queue and
extract inception features
:return: a tuple containing all these operations
"""
# Create the queue operations
image_key, image_tensor, _ = \
queue_single_images_from_folder(self.inputs_dir)
# Build Inception Resnet v2 operations using the image as input
# - from rgb to grayscale to loose the color information
# - from grayscale to rgb just to have 3 identical channels
# - from a [0, 255] int8 range to [-1,+1] float32
# - feed the image into inception and get the embedding
img_for_inception = tf.image.rgb_to_grayscale(image_tensor)
img_for_inception = tf.image.grayscale_to_rgb(img_for_inception)
img_for_inception = prepare_image_for_inception(img_for_inception)
with slim.arg_scope(inception_resnet_v2_arg_scope()):
input_embedding, _ = inception_resnet_v2(img_for_inception,
is_training=False)
operations = image_key, image_tensor, input_embedding
return batch_operations(operations, examples_per_record)
def _create_operations(self, examples_per_record):
image_key, image_tensor, _ = \
queue_single_images_from_folder(self.inputs_dir)
# Build Inception Resnet v2 operations using the image as input
img_for_inception = tf.image.rgb_to_grayscale(image_tensor)
img_for_inception = tf.image.grayscale_to_rgb(img_for_inception)
img_for_inception = prepare_image_for_inception(img_for_inception)
with slim.arg_scope(inception_resnet_v2_arg_scope()):
input_embedding, _ = inception_resnet_v2(img_for_inception,
is_training=False)
operations = image_key, image_tensor, input_embedding
return batch_operations(operations, examples_per_record)