def parser(record):
features = {
'image':
tf.FixedLenFeature([], tf.string, default_value=""),
'label':
tf.FixedLenFeature((),
tf.int64,
default_value=tf.zeros([], dtype=tf.int64)),
'height':
tf.FixedLenFeature((),
tf.int64,
default_value=tf.zeros([], dtype=tf.int64)),
'width':
tf.FixedLenFeature((),
tf.int64,
default_value=tf.zeros([], dtype=tf.int64))
}
parsed = tf.parse_single_example(record, features)
image = tf.decode_raw(parsed["image"], tf.uint8)
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
width = tf.cast(parsed["width"], tf.int32)
height = tf.cast(parsed["height"], tf.int32)
image = tf.reshape(image, [height, width, 1])
image = tf.image.resize_images(image,
(FLAGS.image_size, FLAGS.image_size))
image = tf.reshape(image, [FLAGS.image_size, FLAGS.image_size, 1])
new_image = image_preprocess.preprocess_image(image, FLAGS.image_size,
FLAGS.image_size)
label = tf.cast(parsed["label"], tf.int32)
return new_image, label
def parser(record):
features = {
'image': tf.FixedLenFeature([], tf.string, default_value=""),
'label': tf.FixedLenFeature((), tf.int64, default_value=tf.zeros([], dtype=tf.int64)),
}
parsed = tf.parse_single_example(record, features)
image = tf.decode_raw(parsed["image"], tf.uint8)
image = tf.reshape(image, [112, 112, 1])
image = tf.image.resize_images(image, (FLAGS.image_size, FLAGS.image_size))
image = tf.image.convert_image_dtype(image, dtype=tf.float32)# convert to [0,1]
image = image_preprocess.preprocess_image(image,FLAGS.image_size, FLAGS.image_size)
label = tf.cast(parsed["label"], tf.int32)
return image, label
