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
image system/array_3d_uint8/1 Input
eye_centers system/eye_positions/1 Input
cropped_image system/array_2d_floats/1 Output

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name Description Type Default Range/Choices
left-eye-x uint32 48
left-eye-y uint32 16
crop-height uint32 80
crop-width uint32 64
right-eye-x uint32 15
right-eye-y uint32 16

The code for this algorithm in Python
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This algorithm performs a RGB to grayscale conversion of an image followed by a face cropping, given the eye center coordinates.

The difference between this algorithm with the one published here (https://www.beat-eu.org/platform/algorithms/tutorial/cropping_rgb/8/) is that for this one the output is system/array_2d_floats/1

This implementation relies on the `Bob <http://www.idiap.ch/software/bob>`_ library.

The inputs are:

  • image: an RGB image as a three-dimensional array of uint8, the first dimension being the number of color planes (3), and the second and third dimensions corresponding to the height and width of the original image, respectively.
  • eye_centers: the coordinates of the eye centers in the original image space

The output cropped_image is a grayscale cropped image as a two-dimensional array of floats (64 bits).

Docutils System Messages

System Message: ERROR/3 (<string>, line 6); backlink

Unknown target name: "bob &lt;http://www.idiap.ch/software/bob&gt;".

Experiments

Updated Name Databases/Protocols Analyzers
smarcel/tpereira/full_isv_multi/2/btas2015_face-periocular_mobio-female_det mobio/1@female tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv_multi/2/btas2015_face-periocular_cpqd-smartphone-male_det cpqd/1@smartphone_male tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_face_cpqd-smartphone-male_det cpqd/1@smartphone_male tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv_multi/2/btas2015_face-periocular_mobio-male_det mobio/1@male tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_face_mobio-male_det mobio/1@male tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv_multi/2/btas2015_face-periocular_cpqd-smartphone-female_det cpqd/1@smartphone_female tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_face_cpqd-smartphone-female_det cpqd/1@smartphone_female tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv_multi/2/btas2015_face-periocular_mobio-female_det mobio/1@female tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_face_mobio-female_det mobio/1@female tutorial/eerhter_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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