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)