BEAT - chichan/mlbphs_comp

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 1

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
features	system/array_2d_floats/1	Output

Parameters 7

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

Name	Description	Type	Default	Range/Choices
regions_cols	Number of regions in column	uint8	8	[1, 100]
lbp_maxradius	Maxmum radius of milti LBP	uint8	1	[1, 10]
regions_rows	Number of regions in row	uint8	8	[1, 100]
lbp_minradius	Minimum radius of milti LBP	uint8	1	[1, 10]
lbp_circular	LBP Circular	bool	True
lbp_uniform	LBP Uniform	bool	True
lbp_neighbor_count	LBP Neighbour count	uint8	8	[8, 32]

import bob
import numpy as np

class Algorithm:

def __init__(self):
		self.lbp_minradius = 1
		self.lbp_maxradius = 10
		self.lbp_neighbor_count = 8
		self.lbp_uniform = True
		self.lbp_circular = True
		self.regions_cols = 8
		self.regions_rows = 8

def setup(self, parameters):
        self.lbp_minradius = parameters.get('lbp_minradius', self.lbp_minradius)
        self.lbp_maxradius  = parameters.get('lbp_maxradius',  self.lbp_maxradius)
        self.lbp_neighbor_count = parameters.get('lbp_neighbor_count', self.lbp_neighbor_count)
        self.lbp_uniform = parameters.get('lbp_uniform', self.lbp_uniform)
        self.lbp_circular  = parameters.get('lbp_circular',  self.lbp_circular)
        self.regions_cols = parameters.get('regions_cols', self.regions_cols)
        self.regions_rows = parameters.get('regions_rows', self.regions_rows)
        self.lbp_rotation_invariant  = False
        self.lbp_compare_to_average = False
        self.lbp_add_average = False
        self.lbp_extractors = [bob.ip.LBP(np.int(self.lbp_neighbor_count), np.float(r), 
                                          self.lbp_circular, False, False, 
                                          self.lbp_uniform, False) for r in xrange(self.lbp_minradius, self.lbp_maxradius+1)]    
        return True

def process(self, inputs, outputs):
        cropped_image = inputs["image"].data.value
        regions_width = (cropped_image.shape[1]-2*self.lbp_maxradius)/self.regions_cols
        regions_height = (cropped_image.shape[0]-2*self.lbp_maxradius)/self.regions_rows
        features = np.empty([self.regions_cols*self.regions_rows,0])
        assert(features.shape[0]!=self.regions_cols*self.regions_rows,'features.shape[0]!=self.regions_cols*self.regions_rows')
        std_lbp_output_image = np.ndarray(self.lbp_extractors[self.lbp_maxradius-1].get_lbp_shape(cropped_image), dtype = np.uint16 )
        for i in xrange(len(self.lbp_extractors)):
            lbp_output_image = np.ndarray(self.lbp_extractors[i].get_lbp_shape(cropped_image), dtype = np.uint16 )
            self.lbp_extractors[i](cropped_image,lbp_output_image)
            lbp_output_image = bob.ip.crop(lbp_output_image,
                                           int(self.lbp_maxradius-self.lbp_extractors[i].radius),
                                           int(self.lbp_maxradius-self.lbp_extractors[i].radius),
                                           std_lbp_output_image.shape[0],std_lbp_output_image.shape[1])
            lbp_img_blks = bob.ip.block(lbp_output_image,regions_height,regions_width,0,0)
            lbp_img_blks = lbp_img_blks.reshape(lbp_img_blks.shape[0]*lbp_img_blks.shape[1],lbp_img_blks.shape[2],lbp_img_blks.shape[3])
            lbp_shists = np.empty([self.regions_cols*self.regions_rows,self.lbp_extractors[i].max_label])
            for j in xrange(lbp_img_blks.shape[0]):
                lbp_hist = np.float32(bob.ip.histogram(lbp_img_blks[j],self.lbp_extractors[i].max_label-1))
                lbp_hist /= lbp_hist.sum()
                lbp_shists[j] = lbp_hist
            features = np.hstack((features,lbp_shists))

#        if features.shape[1]==118:
#            print a
#       if features.shape[0]==64:
#           print b
        assert(features.shape[1]!=59,'features.shape[1]!=59')
        assert(features.shape[1]!=self.lbp_extractors[i].max_label*len(self.lbp_extractors),'number of radii not match')
#        assert(features.shape[0]!=64,'features.shape[0]!=64')
#        assert(features.shape[1]!=59,'features.shape[1]!=59')

outputs["features"].write({
                "value" : features
            })
        return True

The code for this algorithm in Python
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Refer to: Chi-Ho Chan, Josef Kittler, Kieron Messer: Multi-scale Local Binary Pattern Histograms for Face Recognition. 809-818

Experiments

Attestation:

Privacy:

Status:

Name	Databases/Protocols	Analyzers
smarcel/chichan/full_pre_mlbphs_projection/2/mobio-f_TT_MLBPH_PCA98_LDA300_postperf-iso	mobio/2@female	tutorial/eerhter_postperf_iso/1
smarcel/chichan/full_pre_mlbphs_projection/2/mobio-m_TT_MLBPH_PCA98_LDA300_postperf-iso	mobio/2@male	tutorial/eerhter_postperf_iso/1
smarcel/chichan/full_pre_mlbphs_projection/2/mobio-m_TT_MLBPH_PCA98_postperf-iso	mobio/2@male	tutorial/eerhter_postperf_iso/1
smarcel/chichan/full_pre_mlbphs_projection/2/mobio-f_TT_MLBPH_PCA98_postperf-iso	mobio/2@female	tutorial/eerhter_postperf_iso/1
chichan/chichan/full_pre_mlbphs_projection/2/Prep_MLBPH_XM2VTS_nouniform_PCA	xm2vts/1@darkened-lp1,xm2vts/1@lp1	tutorial/eerhter_postperf/1
chichan/chichan/full_pre_mlbphs_projection/2/Prep_MLBPH_XM2VTS_LDA	xm2vts/1@darkened-lp1,xm2vts/1@lp1	tutorial/eerhter_postperf/1
chichan/chichan/full_pre_mlbphs_projection/2/Prep_MLBPH_XM2VTS_no_uniform_p98LDA	xm2vts/1@darkened-lp1,xm2vts/1@lp1	tutorial/eerhter_postperf/1
chichan/chichan/full_pre_mlbphs_projection/2/Prep_MLBPH_XM2VTS_PCA	xm2vts/1@darkened-lp1,xm2vts/1@lp1	tutorial/eerhter_postperf/1

Scientific Python 2.7 (0.0.4) 712

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 chichan mlbphs_comp_features 32

Endpoint Groups 1

Group: main

Parameters 7

Experiments

algorithms

chichan

mlbphs_comp_features

32