BEAT - tutorial/linear_machine

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

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: main

Endpoint Name	Data Format	Nature
image	system/array_2d_uint8/1	Input
id	system/uint64/1	Input
projections	system/array_2d_floats/1	Output

Unnamed group

Endpoint Name	Data Format	Nature
subspace	tutorial/linear_machine/1	Input

import bob
import numpy

def linear_machine_from_data(data):
    """Unmangles a bob.machine.LinearMachine from a BEAT Data object"""

machine = bob.machine.LinearMachine(data.weights)
    machine.biases = data.biases
    machine.input_subtract = data.input_subtract
    machine.input_divide = data.input_divide
    return machine

class Algorithm:

def __init__(self):
        self.machine            = None
        self.projections        = None
        self.current_projection = 0

def process(self, inputs, outputs):

# retrieve the linear machine once
        if self.machine is None:
            inputs['subspace'].next()
            self.machine = linear_machine_from_data(inputs['subspace'].data)

# collect all the image projections for the current template
        image = inputs['image'].data.value.astype('float64').flatten()
        projection = self.machine.forward(image)

if self.projections is None:
            group = inputs.groupOf('id')
            nb_images = inputs["id"].data_index_end - inputs["id"].data_index + 1
            self.projections = numpy.ndarray((nb_images, projection.shape[0]), dtype='float64')
            self.current_projection = 0

self.projections[self.current_projection,:] = projection
        self.current_projection += 1

# generate the results (when all the images of the current template have been
        # projected)
        if inputs["id"].isDataUnitDone():
            outputs['projections'].write({
                'value': self.projections
            })

self.projections = None

return True

The code for this algorithm in Python
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This algorithm linearizes and accumulates images into a buffer, before applying a linear transformation (e.g. using a projection matrix computed by principal component analysis). The linear transformation relies on the Bob library.

The inputs are:

image: an image as a two-dimensional arrays of floats (64 bits)
id: an identifier which is used as follows: all images with the

same identifier are accumulated into the same buffer
subspace: a linear transformation as a collection of weights,

biases, input subtraction and input division factors.

The output projections is a two-dimensional array of floats (64 bits), the number of rows corresponding to the number of accumulated images (with the same identifier), and the number of columns to the output dimensionality after applying the linear transformation.

Experiments

Attestation:

Privacy:

Status:

Name	Databases/Protocols	Analyzers
smarcel/tutorial/eigenface_with_preprocessing/1/eigenface-prepro-tutorial-pca15-ter	atnt/1@idiap	tutorial/postperf_iso/1
anjos/tutorial/eigenface_with_preprocessing/1/foorbar	atnt/1@idiap	tutorial/postperf_iso/1
pranayd/pranayd/pranay/1/pranay	atnt/1@idiap	tutorial/postperf_iso/1
pkorshunov/tutorial/eigenface/1/eigenface-with-8-components	atnt/1@idiap	tutorial/postperf_iso/1
pedro/tutorial/eigenface_with_preprocessing/1/eigenface-with-51-components	atnt/1@idiap	tutorial/postperf_iso/1
EderKapisch/EderKapisch/eigenface_preproc/1/eigenface-with-10-components	atnt/1@idiap	tutorial/postperf_iso/1
tutorial/tutorial/eigenface_with_preprocessing/1/eigenface-with-10-components-preproc	atnt/1@idiap	tutorial/postperf_iso/1
AntonioCandia/AntonioCandia/eigenface_preprop/1/eigenface-with-10-components	atnt/1@idiap	tutorial/postperf/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
tutorial/tutorial/eigenface_with_preprocessing/1/eigenface-with-preproc-15	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
tutorial/tutorial/full_eigenface/1/mobioMale_eigenfaces_50comp	mobio/1@male	tutorial/eerhter_postperf_iso/1
anjos/tutorial/eigenface/1/demo42	atnt/1@idiap	tutorial/postperf/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_machine_projection 3

Endpoint Groups 2

Group: main

Unnamed group

Experiments

algorithms

tutorial

linear_machine_projection

3