ssim_multiscale

Multiscale Structural Similarity metric.

Classes

SSIM_Multiscale

Multiscale Structural Similarity.

class ashpy.metrics.ssim_multiscale.SSIM_Multiscale(name='SSIM_Multiscale', model_selection_operator=<built-in function lt>, logdir=PosixPath('/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/master/docs/source/log'), max_val=2.0, power_factors=None, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03)

Bases: ashpy.metrics.metric.Metric

Multiscale Structural Similarity.

See Multiscale structural similarity for image quality assessment [1]

[1]	Multiscale structural similarity for image quality assessment https://ieeexplore.ieee.org/document/1292216

__init__(name='SSIM_Multiscale', model_selection_operator=<built-in function lt>, logdir=PosixPath('/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/master/docs/source/log'), max_val=2.0, power_factors=None, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03)

Initialize the Metric.

Parameters:

• name (str) – Name of the metric.
• model_selection_operator (typing.Callable) –

  The operation that will be used when model_selection is triggered to compare the metrics, used by the update_state. Any typing.Callable behaving like an operator is accepted.

  Note

  Model selection is done ONLY if an operator is specified here.

• logdir (str) – Path to the log dir, defaults to a log folder in the current directory.
• max_val (float) – The dynamic range of the images (i.e., the difference between the maximum the and minimum) (see www.tensorflow.org/versions/r2.0/api_docs/python/tf/image/ssim_multiscale)
• power_factors (List[float]) – Iterable of weights for each of the scales. The number of scales used is the length of the list. Index 0 is the unscaled resolution’s weight and each increasing scale corresponds to the image being downsampled by 2. Defaults to (0.0448, 0.2856, 0.3001, 0.2363, 0.1333), which are the values obtained in the original paper.
• filter_size (int) – Default value 11 (size of gaussian filter).
• filter_sigma (float) – Default value 1.5 (width of gaussian filter).
• k1 (float) – Default value 0.01.
• k2 (float) – Default value 0.03 (SSIM is less sensitivity to K2 for lower values, so it would be better if we take the values in range of 0< K2 <0.4).

Return type:

None

static split_batch(batch)

Split a batch along axis 0 into two tensors having the same size.

Parameters:batch (tf.Tensor) – A batch of images. Return type:Tuple[Tensor, Tensor] Returns:(Tuple[tf.Tensor, tf.Tensor]) The batch split in two tensors. Raises:ValueError – if the batch has size 1.

update_state(context)

Update the internal state of the metric, using the information from the context object.

Parameters:context (ashpy.contexts.gan.GANContext) – An AshPy Context Object that carries all the information the Metric needs. Return type:None