BEAT - tutorial/linear_machines

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

Endpoint Groups 2

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: probes

Endpoint Name	Data Format	Nature
probe_projections	system/array_2d_floats/1	Input
comparison_ids	system/array_1d_uint64/1	Input
probe_id	system/uint64/1	Input
probe_client_id	system/uint64/1	Input
scores	tutorial/probe_scores/1	Output

Group: templates

Endpoint Name	Data Format	Nature
template_client_id	system/uint64/1	Input
template_id	system/uint64/1	Input
template_projections	system/array_2d_floats/1	Input

import bob
import numpy
import scipy.spatial

class Algorithm:

def __init__(self):
        self.templates = None

def process(self, inputs, outputs):

# retrieve all the templates once
        if self.templates is None:

self.templates = {}
            group = inputs.groupOf('template_projections')

while group.hasMoreData():
                group.next()

templates_id = group['template_id'].data.value

self.templates[templates_id] = dict(
                    client_id = group['template_client_id'].data.value,
                    projections = group['template_projections'].data.value,
                )

# process the probe
        comparison_ids  = inputs['comparison_ids'].data.value
        projections     = inputs['probe_projections'].data.value

scores = []
        for comparison_id in comparison_ids:
            template_client_identity = self.templates[comparison_id]['client_id']

distances = []

# gets the min distance for all subprobes w.r.t to all subtemplates
            for subprobe in projections:
                distances.append(numpy.min([scipy.spatial.distance.euclidean(k, subprobe)
                                            for k in self.templates[comparison_id]['projections']]))

# gets the min distance and inverts the scores so matching
            # numbers are numerically bigger than non-matching ones
            scores.append({
                'template_identity': self.templates[comparison_id]['client_id'],
                'score': -numpy.min(distances),
            })

if inputs['probe_id'].isDataUnitDone():
            outputs['scores'].write({
                    'client_identity': inputs['probe_client_id'].data.value,
                    'scores': scores
                },
                end_data_index=inputs['probe_id'].data_index_end
            )

return True

The code for this algorithm in Python
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Could not find any documentation for this object.

Experiments

Attestation:

Privacy:

Status:

Name	Databases/Protocols	Analyzers
pkorshunov/tutorial/eigenface/1/eigenface-with-8-components	atnt/1@idiap	tutorial/postperf_iso/1
jastuchi/tutorial/eigenface/1/eigenface-with-11-components	atnt/1@idiap	tutorial/postperf_iso/1
marcus/tutorial/eigenface/1/eigenface-with-23-components	atnt/1@idiap	tutorial/postperf_iso/1
murilovarges/tutorial/eigenface/1/eigenfaces_15comp_unesp	atnt/1@idiap	tutorial/postperf_iso/1
kgrm/tutorial/eigenface/1/eigenfaces_11comp	atnt/1@idiap	tutorial/postperf_iso/1
anjos/tutorial/eigenface/1/demo42	atnt/1@idiap	tutorial/postperf/1
tutorial/tutorial/full_fisherface/1/fullfisherfaces_10and4ncomp	atnt/1@idiap_test_eyepos	tutorial/eerhter_postperf_iso/1
tutorial/tutorial/full_fisherface/1/fullfisherfaces_5and2ncomp	atnt/1@idiap_test_eyepos	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.

algorithms tutorial linear_machines_scoring 1

Endpoint Groups 2

Group: probes

Group: templates

Experiments

algorithms

tutorial

linear_machines_scoring

1