def parse_img_with_aug(img):
img = tf.io.decode_image(img,
channels=all_var_dict['target_shape'][-1],
dtype=tf.dtypes.float32,
expand_animations=False)
img = augment_img(img, 16, 200, all_var_dict['target_shape'])
# img = tf.image.resize_with_pad(img, all_var_dict['target_shape'][1],
# all_var_dict['target_shape'][0])
return img
def parse_model1(path, label):
img = tf.io.read_file(path)
img = parse_img(img)
if AUGMENT:
img = metadata.augment_img(img, 6, target_shape)
label = all_var_dict['ok_lookup'].lookup(label)
onehot_label = tf.one_hot(label, all_var_dict['LABEL_NUM'], dtype='int64')
onehot_label = tf.cast(onehot_label, dtype=tf.float32)
return img, onehot_label
def parse_other_com_to_morecomp(path):
img = tf.io.read_file(path)
img = parse_img(img)
if AUGMENT:
img = metadata.augment_img(img, 6, target_shape)
label = all_var_dict['ok_lookup'].lookup(
tf.constant('NG-MoreComp', dtype=tf.string))
onehot_label = tf.one_hot(label, all_var_dict['LABEL_NUM'], dtype='int64')
onehot_label = tf.cast(onehot_label, dtype=tf.float32)
return img, onehot_label
def process_ng(path, degree):
byte_string_img = tf.io.read_file(path)
img = tf.io.decode_image(byte_string_img,
channels=target_shape[-1],
dtype=tf.dtypes.float32)
# img = tf.image.convert_image_dtype(img, tf.float32)
img = tf.image.resize_with_crop_or_pad(img, target_shape[1],
target_shape[0])
if AUGMENT:
img = metadata.augment_img(img, 6, target_shape)
deg = ng_lookup.lookup(degree)
onehot_degree = tf.one_hot(deg, DEGREE_NUM, dtype='int64')
return img, onehot_degree