Function per_image_standardization

tensorflow/python/ops/image_ops_impl.py:1481–1522

Linearly scales each image in `image` to have mean 0 and variance 1. For each 3-D image `x` in `image`, computes `(x - mean) / adjusted_stddev`, where - `mean` is the average of all values in `x` - `adjusted_stddev = max(stddev, 1.0/sqrt(N))` is capped away from 0 to protect against di

(image)

@tf_export('image.per_image_standardization')
def per_image_standardization(image):
  """Linearly scales each image in `image` to have mean 0 and variance 1.

  For each 3-D image `x` in `image`, computes `(x - mean) / adjusted_stddev`,
  where

  - `mean` is the average of all values in `x`
  - `adjusted_stddev = max(stddev, 1.0/sqrt(N))` is capped away from 0 to
    protect against division by 0 when handling uniform images
    - `N` is the number of elements in `x`
    - `stddev` is the standard deviation of all values in `x`

  Args:
    image: An n-D Tensor with at least 3 dimensions, the last 3 of which are the
      dimensions of each image.

  Returns:
    A `Tensor` with same shape and dtype as `image`.

  Raises:
    ValueError: if the shape of 'image' is incompatible with this function.
  """
  with ops.name_scope(None, 'per_image_standardization', [image]) as scope:
    image = ops.convert_to_tensor(image, name='image')
    image = _AssertAtLeast3DImage(image)

    # Remember original dtype to so we can convert back if needed
    orig_dtype = image.dtype
    if orig_dtype not in [dtypes.float16, dtypes.float32]:
      image = convert_image_dtype(image, dtypes.float32)

    num_pixels = math_ops.reduce_prod(array_ops.shape(image)[-3:])
    image_mean = math_ops.reduce_mean(image, axis=[-1, -2, -3], keepdims=True)

    # Apply a minimum normalization that protects us against uniform images.
    stddev = math_ops.reduce_std(image, axis=[-1, -2, -3], keepdims=True)
    min_stddev = math_ops.rsqrt(math_ops.cast(num_pixels, image.dtype))
    adjusted_stddev = math_ops.maximum(stddev, min_stddev)

    image -= image_mean
    image = math_ops.div(image, adjusted_stddev, name=scope)
    return convert_image_dtype(image, orig_dtype, saturate=True)


@tf_export('image.random_brightness')