本文整理汇总了Python中tensorflow.python.ops.gen_image_ops.adjust_saturation方法的典型用法代码示例。如果您正苦于以下问题：Python gen_image_ops.adjust_saturation方法的具体用法？Python gen_image_ops.adjust_saturation怎么用？Python gen_image_ops.adjust_saturation使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.ops.gen_image_ops的用法示例。
在下文中一共展示了gen_image_ops.adjust_saturation方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: random_saturation
# 需要导入模块: from tensorflow.python.ops import gen_image_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_image_ops import adjust_saturation [as 别名]
def random_saturation(image, lower, upper, seed=None):
"""Adjust the saturation of an RGB image by a random factor.
Equivalent to `adjust_saturation()` but uses a `saturation_factor` randomly
picked in the interval `[lower, upper]`.
Args:
image: RGB image or images. Size of the last dimension must be 3.
lower: float. Lower bound for the random saturation factor.
upper: float. Upper bound for the random saturation factor.
seed: An operation-specific seed. It will be used in conjunction
with the graph-level seed to determine the real seeds that will be
used in this operation. Please see the documentation of
set_random_seed for its interaction with the graph-level random seed.
Returns:
Adjusted image(s), same shape and DType as `image`.
Raises:
ValueError: if `upper <= lower` or if `lower < 0`.
"""
if upper <= lower:
raise ValueError('upper must be > lower.')
if lower < 0:
raise ValueError('lower must be non-negative.')
# Pick a float in [lower, upper]
saturation_factor = random_ops.random_uniform([], lower, upper, seed=seed)
return adjust_saturation(image, saturation_factor) 示例2: adjust_saturation
# 需要导入模块: from tensorflow.python.ops import gen_image_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_image_ops import adjust_saturation [as 别名]
def adjust_saturation(image, saturation_factor, name=None):
"""Adjust saturation of an RGB image.
This is a convenience method that converts an RGB image to float
representation, converts it to HSV, add an offset to the saturation channel,
converts back to RGB and then back to the original data type. If several
adjustments are chained it is advisable to minimize the number of redundant
conversions.
`image` is an RGB image. The image saturation is adjusted by converting the
image to HSV and multiplying the saturation (S) channel by
`saturation_factor` and clipping. The image is then converted back to RGB.
Args:
image: RGB image or images. Size of the last dimension must be 3.
saturation_factor: float. Factor to multiply the saturation by.
name: A name for this operation (optional).
Returns:
Adjusted image(s), same shape and DType as `image`.
"""
with ops.name_scope(name, 'adjust_saturation', [image]) as name:
image = ops.convert_to_tensor(image, name='image')
# Remember original dtype to so we can convert back if needed
orig_dtype = image.dtype
flt_image = convert_image_dtype(image, dtypes.float32)
# TODO(zhengxq): we will switch to the fused version after we add a GPU
# kernel for that.
fused = os.environ.get('TF_ADJUST_SATURATION_FUSED', '')
fused = fused.lower() in ('true', 't', '1')
if fused:
return convert_image_dtype(
gen_image_ops.adjust_saturation(flt_image, saturation_factor),
orig_dtype)
hsv = gen_image_ops.rgb_to_hsv(flt_image)
hue = array_ops.slice(hsv, [0, 0, 0], [-1, -1, 1])
saturation = array_ops.slice(hsv, [0, 0, 1], [-1, -1, 1])
value = array_ops.slice(hsv, [0, 0, 2], [-1, -1, 1])
saturation *= saturation_factor
saturation = clip_ops.clip_by_value(saturation, 0.0, 1.0)
hsv_altered = array_ops.concat([hue, saturation, value], 2)
rgb_altered = gen_image_ops.hsv_to_rgb(hsv_altered)
return convert_image_dtype(rgb_altered, orig_dtype)