shannon_entropy(image, base=2)

The Shannon entropy is defined as S = -sum(pk * log(pk)), where pk are frequency/probability of pixels of value k.

Notes

The returned value is measured in bits or shannon (Sh) for base=2, natural unit (nat) for base=np.e and hartley (Hart) for base=10.

Parameters

image : (N, M) ndarray

Grayscale input image.

base : float, optional

The logarithmic base to use.

Returns

entropy : float

Calculate the Shannon entropy of an image.

Examples

This example is valid syntax, but we were not able to check execution

>>> from skimage import data
... shannon_entropy(data.camera())
7.047955232423086

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

skimage.measure.entropy.shannon_entropy

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

---

File: /skimage/measure/entropy.py#5
type: <class 'function'>
Commit: