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fdi-pyEmbed-v1 (Face Detection Image)

Version Changelog

Plugin Version Change
v1.0.0 Initial plugin, released with OLIVE 5.3.0

Description

Face Detection Image plugins process an input image and attempt to localize one or more faces within the frame. If they are detected, a 'bounding box' highlighting the face is output, along with an associated confidence score informing how likely this box is to be a face.

Unlike Face Recognition plugins, a Face Detection plugin is only looking for faces generically - it doesn't care about the identity of the face or faces, and will report any faces detected.

Domains

  • multi-v1
    • A general purpose image processing domain.

Inputs

An image file to process.

Outputs

Face Detection Image plugins are 'bounding box' scorers - the output of a bounding box scorer is a class, a corresponding score, and 4 points associated with this class and score that attempt to localize the detected class (in this case a face) within the image frame.

That output looks like this:

    <file> <class> <score> (<x1>, <y1>, <x2>, <y2>)

Where the bounding box itself is defined by the four coordinates in parentheses:

    (Upper Left: x1, y1    |    Lower Right: x2, y2)

An example output could look like this:

    input_image.png face 0.9974257349967957 (154, 78, 657, 745)

Functionality (Traits)

The functions of this plugin are defined by its Traits and implemented API messages. A list of these Traits is below, along with the corresponding API messages for each. Click the message name below to go to additional implementation details below.

  • BOUNDING_BOX_SCORER (NOTE: Coming Soon) – Score all submitted images or videos, returning labeled bounding box regions within the image frames, or labeled bounding box regions with an associated start and end time region if scoring video files.

Compatibility

OLIVE 5.3+

Limitations

Comments

Global Options

This plugin does not currently have user-configurable options, though it is possible for some performance tweaks and configuration changes to be made. If you find this plugin to not perform adequately for your data conditions, or have a specific use case, please get in touch with SRI to discuss how the plugin can be tuned for optimal performance on your data.