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