Histogram equalization for grayscale image

This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.
This algorithm is splittable

Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.

Group: main

Endpoint Name Data Format Nature
raw_image system/array_2d_uint8/1 Input
image system/array_2d_uint8/1 Output

The code for this algorithm in Python
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This algorithm implements histogram equalization for grayscale image.

The implementation is based on the following blog.

Experiments

Updated Name Databases/Protocols Analyzers
omenemac/omenemac/gagatoolchainfork/1/MacsExperiment4 atnt/2@idiap tutorial/postperf_iso/1

This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.

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