本文整理汇总了Python中tensorflow.python.ops.gen_image_ops._adjust_contrastv2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_contrastv2方法的具体用法？Python gen_image_ops._adjust_contrastv2怎么用？Python gen_image_ops._adjust_contrastv2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_contrastv2方法的1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: adjust_contrast
# 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_contrastv2 [as 别名]
def adjust_contrast(images, contrast_factor):
"""Adjust contrast of RGB or grayscale images.
This is a convenience method that converts an RGB image to float
representation, adjusts its contrast, and then converts it back to the
original data type. If several adjustments are chained it is advisable to
minimize the number of redundant conversions.
`images` is a tensor of at least 3 dimensions. The last 3 dimensions are
interpreted as `[height, width, channels]`. The other dimensions only
represent a collection of images, such as `[batch, height, width, channels].`
Contrast is adjusted independently for each channel of each image.
For each channel, this Op computes the mean of the image pixels in the
channel and then adjusts each component `x` of each pixel to
`(x - mean) * contrast_factor + mean`.
Args:
images: Images to adjust. At least 3-D.
contrast_factor: A float multiplier for adjusting contrast.
Returns:
The contrast-adjusted image or images.
"""
with ops.name_scope(None, 'adjust_contrast',
[images, contrast_factor]) as name:
images = ops.convert_to_tensor(images, name='images')
# Remember original dtype to so we can convert back if needed
orig_dtype = images.dtype
flt_images = convert_image_dtype(images, dtypes.float32)
# pylint: disable=protected-access
adjusted = gen_image_ops._adjust_contrastv2(flt_images,
contrast_factor=contrast_factor,
name=name)
# pylint: enable=protected-access
return convert_image_dtype(adjusted, orig_dtype, saturate=True)