BEAT - tutorial/linear_machine_x2

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: 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_lda	tutorial/linear_machine/1	Input
subspace_pca	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.pca_machine        = None
        self.lda_machine        = None
        self.projections        = None
        self.current_projection = 0

def process(self, inputs, outputs):

# retrieve the linear machines once
        if self.pca_machine is None:
            inputs['subspace_pca'].next()
            self.pca_machine = linear_machine_from_data(inputs['subspace_pca'].data)
        if self.lda_machine is None:
            inputs['subspace_lda'].next()
            self.lda_machine = linear_machine_from_data(inputs['subspace_lda'].data)

# collect all the image projections for the current template
        image = inputs['image'].data.value.astype('float64').flatten()
        projection = self.lda_machine.forward(self.pca_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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Could not find any documentation for this object.

Experiments

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Status:

			Updated	Name	Databases/Protocols	Analyzers
				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

Scientific Python 2.7 (0.0.3) 12

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_x2_projection 1

Endpoint Groups 2

Group: main

Unnamed group

Experiments

algorithms

tutorial

linear_machine_x2_projection

1