def translate_y(image):
level = tf.convert_to_tensor(
(config.AUGMENT_MAGNITUDE / config._MAX_LEVEL) *
float(config.TRANSLATE_CONST), tf.float32)
should_flip = tf.cast(tf.floor(tf.random.uniform([]) + 0.5),
tf.bool) # 得到的结果为True和False
pixels = tf.cond(should_flip, lambda: level, lambda: -level)
image = image_ops.translate(image, [0, -pixels])
return image
def translate_y(image, pixels, replace):
"""Equivalent of PIL Translate in Y dimension."""
image = contrib_image.translate(wrap(image), [0, -pixels])
return unwrap(image, replace)
def translate_y(image, pixels, fill_value):
"""Equivalent of PIL Translate in Y dimension."""
image = tfi.translate(wrap(image), [0, -pixels])
return unwrap(image, fill_value)
def translate_x(image, pixels, replace): """Equivalent of PIL Translate in X dimension.""" image = image_ops.translate(wrap(image), [-pixels, 0]) return unwrap(image, replace)
def translate_x(image, pixels, replace):
'''Equivalent of PIL Translate in X dimension.'''
image = contrib_image.translate(wrap(image), [-pixels, 0])
return unwrap(image, replace)
def translate_y(image, pixels, replace):
"""Equivalent of PIL Translate in Y dimension."""
with tf.name_scope("translate_y"):
image = image_ops.translate(wrap(image), [0, -pixels])
return unwrap(image, replace)
