[many] More clean-ups and code re-organization

README.rst

 .. vim: set fileencoding=utf-8 :
-.. Andre Anjos <andre.anjos@idiap.ch>
 .. Fri 08 Jul 2016 15:38:56 CEST
 
 .. image:: http://img.shields.io/badge/docs-stable-yellow.png

bob/bio/vein/algorithms/HammingDistance.py

-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Pedro Tome <Pedro.Tome@idiap.ch>
-
-import bob.ip.base
-import bob.sp
-
-import numpy
-import math
-import scipy.signal
-
-from facereclib.tools.Tool import Tool
-
-class MiuraMatch (Tool):
-  """Finger vein matching: match ratio based on
-     N. Miura, A. Nagasaka, and T. Miyatake. Feature extraction of finger vein patterns based on repeated line tracking and its application 
-     to personal identification. Machine Vision and Applications, Vol. 15, Num. 4, pp. 194--203, 2004
-  """
-
-  def __init__(
-      self,
-      # some similarity functions might need a GaborWaveletTransform class, so we have to provide the parameters here as well...
-      ch = 8,       # Maximum search displacement in y-direction
-      cw = 5,       # Maximum search displacement in x-direction
-      gpu = False,
-  ):
-
-    # call base class constructor
-    Tool.__init__(
-        self,
-
-        ch = ch,
-        cw = cw,
-
-        multiple_model_scoring = None,
-        multiple_probe_scoring = None
-    )
-
-    self.ch = ch
-    self.cw = cw
-    self.gpu = gpu
-
-  def enroll(self, enroll_features):
-    """Enrolls the model by computing an average graph for each model"""
-    # return the generated model
-    return numpy.vstack(enroll_features)
-
-
-  def score(self, model, probe):
-    """Computes the score of the probe and the model
-         Return score - Value between 0 and 0.5, larger value is better match
-    """
-    I=probe.astype(numpy.float64)
-    R=model.astype(numpy.float64)
-    h, w = R.shape
-    crop_R = R[self.ch:h-self.ch, self.cw:w-self.cw]
-    rotate_R = numpy.zeros((crop_R.shape[0], crop_R.shape[1]))
-    bob.ip.base.rotate(crop_R, rotate_R, 180)
-    #FFT for scoring!   
-    #Nm=bob.sp.ifft(bob.sp.fft(I)*bob.sp.fft(rotate_R))
-    if self.gpu == True:
-        import xbob.cusp
-        Nm = xbob.cusp.conv(I, rotate_R);
-    else:
-        Nm = scipy.signal.convolve2d(I, rotate_R, 'valid');
-    t0, s0 = numpy.unravel_index(Nm.argmax(), Nm.shape)
-    Nmm = Nm[t0,s0]
-    #Nmm = Nm.max()
-    #mi = numpy.argwhere(Nmm == Nm) 
-    #t0, s0 = mi.flatten()[:2]
-    score = Nmm/(sum(sum(crop_R)) + sum(sum(I[t0:t0+h-2*self.ch, s0:s0+w-2*self.cw])))
-    return score
-    

bob/bio/vein/algorithms/MiuraMatch.py

 #!/usr/bin/env python
 # vim: set fileencoding=utf-8 :
-# Pedro Tome <Pedro.Tome@idiap.ch>
 
 import bob.sp
 import bob.ip.base
@@ -9,24 +8,27 @@ import numpy
 import math
 import scipy.signal
 
-from facereclib.tools.Tool import Tool
+from bob.bio.base.algorithm import Algorithm
 
-class MiuraMatch (Tool):
-  """Finger vein matching: match ratio based on
-     N. Miura, A. Nagasaka, and T. Miyatake. Feature extraction of finger vein patterns based on repeated line tracking and its application 
-     to personal identification. Machine Vision and Applications, Vol. 15, Num. 4, pp. 194--203, 2004
+
+class MiuraMatch (Algorithm):
+  """Finger vein matching: match ratio
+
+  Based on N. Miura, A. Nagasaka, and T. Miyatake. Feature extraction of finger
+  vein patterns based on repeated line tracking and its application to personal
+  identification. Machine Vision and Applications, Vol. 15, Num. 4, pp.
+  194--203, 2004
   """
 
-  def __init__(
-      self,
+  def __init__(self,
       # some similarity functions might need a GaborWaveletTransform class, so we have to provide the parameters here as well...
       ch = 8,       # Maximum search displacement in y-direction
       cw = 5,       # Maximum search displacement in x-direction
       gpu = False,
-  ):
+      ):
 
     # call base class constructor
-    Tool.__init__(
+    Algorithm.__init__(
         self,
 
         ch = ch,
@@ -40,8 +42,10 @@ class MiuraMatch (Tool):
     self.cw = cw
     self.gpu = gpu
 
+
   def enroll(self, enroll_features):
     """Enrolls the model by computing an average graph for each model"""
+
     # return the generated model
     #import ipdb; ipdb.set_trace()
     return numpy.array(enroll_features)
@@ -52,7 +56,7 @@ class MiuraMatch (Tool):
     size_t  = numpy.array(t.shape)
     size_a  = numpy.array(a.shape)
     outsize = size_t + size_a - 1
-    
+
     # Determine 2D cross correlation in Fourier domain
     taux = numpy.zeros(outsize)
     taux[0:size_t[0],0:size_t[1]] = t
@@ -60,12 +64,12 @@ class MiuraMatch (Tool):
     aaux = numpy.zeros(outsize)
     aaux[0:size_a[0],0:size_a[1]] = a
     Fa = bob.sp.fft(aaux.astype(numpy.complex128))
-    
+
     convta = numpy.real(bob.sp.ifft(Ft*Fa))
-    
+
     [w, h] = size_t-size_a+1
     output = convta[size_a[0]-1:size_a[0]-1+w, size_a[1]-1:size_a[1]-1+h]
-        
+
     return output
 
 
@@ -75,12 +79,12 @@ class MiuraMatch (Tool):
     """
     #print model.shape
     #print probe.shape
-    
+
     I=probe.astype(numpy.float64)
-    
-    if len(model.shape) == 2: 
+
+    if len(model.shape) == 2:
       model = numpy.array([model])
-    
+
     n_models = model.shape[0]
 
     scores = []
@@ -90,7 +94,7 @@ class MiuraMatch (Tool):
       crop_R = R[self.ch:h-self.ch, self.cw:w-self.cw]
       rotate_R = numpy.zeros((crop_R.shape[0], crop_R.shape[1]))
       bob.ip.base.rotate(crop_R, rotate_R, 180)
-      #FFT for scoring!   
+      #FFT for scoring!
       #Nm=bob.sp.ifft(bob.sp.fft(I)*bob.sp.fft(rotate_R))
       if self.gpu == True:
           Nm = self.convfft(I, rotate_R)
@@ -99,13 +103,12 @@ class MiuraMatch (Tool):
       else:
           Nm = self.convfft(I, rotate_R)
           #Nm2 = scipy.signal.convolve2d(I, rotate_R, 'valid')
-          
+
       t0, s0 = numpy.unravel_index(Nm.argmax(), Nm.shape)
       Nmm = Nm[t0,s0]
       #Nmm = Nm.max()
-      #mi = numpy.argwhere(Nmm == Nm) 
+      #mi = numpy.argwhere(Nmm == Nm)
       #t0, s0 = mi.flatten()[:2]
       scores.append(Nmm/(sum(sum(crop_R)) + sum(sum(I[t0:t0+h-2*self.ch, s0:s0+w-2*self.cw]))))
-        
+
     return numpy.mean(scores)
-    

bob/bio/vein/algorithms/__init__.py

-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-
-"""Tool chain for computing verification scores"""
-
-from MiuraMatch import MiuraMatch

bob/bio/vein/configurations/__init__.py

-#!/usr/bin/env python
-# Pedro Tome <Pedro.Tome@idiap.ch>
-
-"""Configuration files for different steps of the fingervein recognition tool chain"""
-
-import databases
-import preprocessing
-import features
-import tools
-import grid
-

bob/bio/vein/configurations/algorithms.py

 #!/usr/bin/env python
 # vim: set fileencoding=utf-8 :
 
-import facereclib
-
-from .. import tools as fingervein_tools
-
-huangwl_tool = fingervein_tools.MiuraMatch(
-    ch = 18,
-    cw = 28,
-)
-
-huangwl_gpu_tool = fingervein_tools.MiuraMatch(
-    ch = 18,
-    cw = 28,
-    gpu = True,
-)
-
-
-miuramax_tool = fingervein_tools.MiuraMatch(
-    ch = 80,
-    cw = 90,
-)
-
-miuramax_gpu_tool = fingervein_tools.MiuraMatch(
-    ch = 80,
-    cw = 90,
-    gpu = True,
-)
-
-miurarlt_tool = fingervein_tools.MiuraMatch(
-    ch = 65,
-    cw = 55,
-)
-
-miurarlt_gpu_tool = fingervein_tools.MiuraMatch(
-    ch = 65,
-    cw = 55,
-    gpu = True,
-)
-
+from ..algorithms import MiuraMatch
+
+huangwl_tool = MiuraMatch(ch=18, cw=28)
+huangwl_gpu_tool = MiuraMatch(ch=18, cw=28, gpu=True)
+miuramax_tool = MiuraMatch(ch=80, cw=90)
+miuramax_gpu_tool = MiuraMatch(ch=80, cw=90, gpu=True)
+miurarlt_tool = MiuraMatch(ch=65, cw=55)
+miurarlt_gpu_tool = MiuraMatch(ch=65, cw=55, gpu=True)

bob/bio/vein/configurations/databases/__init__.py

-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Pedro Tome <pedro.tome@idiap.ch>
-
-"""Configuration files for image databases"""

bob/bio/vein/configurations/databases/utfvp.py

 #!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
 
 import bob.db.utfvp
-import facereclib
+
+from bob.bio.base.database import DatabaseBob
 
 utfvp_directory = "/idiap/resource/database/UTFVP/data/"
 
-database = facereclib.databases.DatabaseBob(
+database = DatabaseBob(
     database = bob.db.utfvp.Database(
       original_directory = utfvp_directory,
       original_extension = ".png"
-    ),
-    name = 'utfvp',    
-)
+      ),
+    name = 'utfvp',
+    )

bob/bio/vein/configurations/databases/vera.py

 #!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
 
 import bob.db.vera
-import facereclib
+
+from bob.bio.base.database import DatabaseBob
 
 vera_directory = "/idiap/project/vera"
 
-database = facereclib.databases.DatabaseBob(
+database = DatabaseBob(
     database = bob.db.vera.Database(
       original_directory = vera_directory,
-      original_extension = ".png",    
-    ),
+      original_extension = ".png",
+      ),
     name = 'vera',
-)
+    )
\ No newline at end of file

bob/bio/vein/configurations/extractors/__init__.py

-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Pedro Tome <Pedro.Tome@idiap.ch>
-
-"""Configuration files for feature extractors"""

bob/bio/vein/configurations/extractors/lbp.py

 #!/usr/bin/env python
 # vim: set fileencoding=utf-8 :
-# Pedro Tome <Pedro.Tome@idiap.ch>
 
-import bob.fingervein
+from ...extractors import LocalBinaryPatterns
 
 # Parameters
 
@@ -18,25 +17,24 @@ LBP_CIRCULAR = True
 LBP_ROTATION_INVARIANT = False
 LBP_COMPARE_TO_AVERAGE = False
 LBP_ADD_AVERAGE = False
+
 # histogram options
 SPARSE_HISTOGRAM = False
 SPLIT_HISTOGRAM = None
-      
 
 
 #Define feature extractor
-feature_extractor = bob.fingervein.features.LocalBinaryPatterns(
-  block_size = BLOCK_SIZE,    # one or two parameters for block size
-  block_overlap = BLOCK_OVERLAP, # one or two parameters for block overlap
-  lbp_radius = LBP_RADIUS,
-  lbp_neighbor_count = LBP_NEIGHBOR_COUNT,
-  lbp_uniform = LBP_UNIFORM,
-  lbp_circular = LBP_CIRCULAR,
-  lbp_rotation_invariant = LBP_ROTATION_INVARIANT,
-  lbp_compare_to_average = LBP_COMPARE_TO_AVERAGE,
-  lbp_add_average = LBP_ADD_AVERAGE,
+feature_extractor = LocalBinaryPatterns(
+  block_size=BLOCK_SIZE,    # one or two parameters for block size
+  block_overlap=BLOCK_OVERLAP, # one or two parameters for block overlap
+  lbp_radius=LBP_RADIUS,
+  lbp_neighbor_count=LBP_NEIGHBOR_COUNT,
+  lbp_uniform=LBP_UNIFORM,
+  lbp_circular=LBP_CIRCULAR,
+  lbp_rotation_invariant=LBP_ROTATION_INVARIANT,
+  lbp_compare_to_average=LBP_COMPARE_TO_AVERAGE,
+  lbp_add_average=LBP_ADD_AVERAGE,
   # histogram options
-  sparse_histogram = SPARSE_HISTOGRAM,
-  split_histogram = SPLIT_HISTOGRAM
+  sparse_histogram=SPARSE_HISTOGRAM,
+  split_histogram=SPLIT_HISTOGRAM
 )
-

bob/bio/vein/configurations/extractors/maximum_curvature.py

 #!/usr/bin/env python
 # vim: set fileencoding=utf-8 :
-# Pedro Tome <Pedro.Tome@idiap.ch>
 
-import bob.fingervein
+from ...extractors import LocalBinaryPatterns
 
-# Parameters 
+
+# Parameters
 SIGMA_DERIVATES = 5 #Sigma used for determining derivatives
 
 GPU_ACCELERATION = False
@@ -13,6 +13,6 @@ GPU_ACCELERATION = False
 feature_extractor = bob.fingervein.features.MaximumCurvature(
 	sigma = SIGMA_DERIVATES,
 	gpu = GPU_ACCELERATION
-	
+
 )
 

bob/bio/vein/configurations/extractors/normalised_crosscorr.py

 #!/usr/bin/env python
 # vim: set fileencoding=utf-8 :
-# Pedro Tome <Pedro.Tome@idiap.ch>
 
-import bob.fingervein
-
-# Parameters 
-
-#Define feature extractor
-feature_extractor = bob.fingervein.features.NormalisedCrossCorrelation(
-	
-)
+from ...extractors import NormalisedCrossCorrelation
 
+feature_extractor = bob.fingervein.features.NormalisedCrossCorrelation()

bob/bio/vein/configurations/extractors/repeated_line_tracking.py

 #!/usr/bin/env python
 # vim: set fileencoding=utf-8 :
-# Pedro Tome <Pedro.Tome@idiap.ch>
 
-import bob.fingervein
+from ...extractors import RepeatedLineTracking
 
-# Parameters
-NUMBER_ITERATIONS = 3000 	# Maximum number of iterations
-DISTANCE_R = 1 			# Distance between tracking point and cross section of the profile
-PROFILE_WIDTH = 21 		# Width of profile
+# Maximum number of iterations
+NUMBER_ITERATIONS = 3000
 
+# Distance between tracking point and cross section of profile
+DISTANCE_R = 1
 
-#Define feature extractor
-feature_extractor = bob.fingervein.features.RepeatedLineTracking(
-	iterations = NUMBER_ITERATIONS, 
-	r = DISTANCE_R,             
-      	profile_w = PROFILE_WIDTH            
-)
+# Width of profile
+PROFILE_WIDTH = 21
 
+
+feature_extractor = RepeatedLineTracking(
+    iterations=NUMBER_ITERATIONS,
+    r=DISTANCE_R,
+    profile_w=PROFILE_WIDTH
+    )

bob/bio/vein/configurations/extractors/wide_line_detector.py

 #!/usr/bin/env python
 # vim: set fileencoding=utf-8 :
-# Pedro Tome <Pedro.Tome@idiap.ch>
 
 import bob.fingervein
 
 # Parameters
 RADIUS_NEIGHBOURHOOD_REGION = 5 # Radius of the circular neighbourhood region
-NEIGHBOURHOOD_THRESHOLD = 1 
-SUM_NEIGHBOURHOOD = 41 		#Sum of neigbourhood threshold
+NEIGHBOURHOOD_THRESHOLD = 1
+SUM_NEIGHBOURHOOD = 41 #Sum of neigbourhood threshold
 RESCALE = True
 
 #Define feature extractor
 feature_extractor = bob.fingervein.features.WideLineDetector(
-	radius = RADIUS_NEIGHBOURHOOD_REGION,
+  radius = RADIUS_NEIGHBOURHOOD_REGION,
       threshold = NEIGHBOURHOOD_THRESHOLD,
-	g = SUM_NEIGHBOURHOOD,
-	rescale = RESCALE
+  g = SUM_NEIGHBOURHOOD,
+  rescale = RESCALE
 )
 

bob/bio/vein/configurations/grid/__init__.py

-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Pedro Tome <Pedro.Tome@idiap.ch>
-
-"""Configuration files for SGE grid executions"""

bob/bio/vein/configurations/grid/demanding.py

 #!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
 
-import facereclib
-
-# define a queue with demanding parameters
-grid = facereclib.utils.GridParameters(
-  training_queue = '32G',
-  # preprocessing
-  number_of_preprocessings_per_job = 200,
-  preprocessing_queue = '4G',
-  # feature extraction
-  number_of_extracted_features_per_job = 200,
-  extraction_queue = '8G',
-  # feature projection
-  number_of_projected_features_per_job = 200,
-  projection_queue = '8G',
-  # model enrollment
-  number_of_enrolled_models_per_job = 10,
-  enrollment_queue = '8G',
-  # scoring
-  number_of_models_per_scoring_job = 10,
-  scoring_queue = '8G'
-)
+import bob.bio.base.grid import Grid
+
+
+grid = Grid(
+    training_queue='32G',
+
+    number_of_preprocessings_per_job=200,
+    preprocessing_queue='4G',
+
+    number_of_extraction_jobs=200,
+    extraction_queue='8G',
+
+    number_of_projection_jobs=200,
+    projection_queue='8G',
+
+    number_of_enrollment_jobs=10,
+    enrollment_queue='8G',
+
+    number_of_scoring_jobs=10,
+    scoring_queue='8G',
+    )

bob/bio/vein/configurations/grid/gbu.py

 #!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
 
-import facereclib
-
-# define a queue specifically for the xbob.db.gbu database
-grid = facereclib.utils.GridParameters(
-  training_queue = '32G',
-  # preprocessing
-  number_of_preprocessings_per_job = 1000,
-  preprocessing_queue = '8G',
-  # feature extraction
-  number_of_extracted_features_per_job = 100,
-  extraction_queue = '8G',
-  # feature projection
-  number_of_projected_features_per_job = 100,
-  projection_queue = '8G',
-  # model enrollment
-  number_of_enrolled_models_per_job = 100,
-  enrollment_queue = '8G',
-  # scoring
-  number_of_models_per_scoring_job = 10,
-  scoring_queue = '8G'
-)
+import bob.bio.base.grid import Grid
 
+
+grid = Grid(
+    training_queue = '32G',
+
+    number_of_preprocessing_jobs = 1000,
+    preprocessing_queue = '8G',
+
+    number_of_extraction_jobs = 100,
+    extraction_queue = '8G',
+
+    number_of_projection_jobs = 100,
+    projection_queue = '8G',
+
+    number_of_enrollment_jobs = 100,
+    enrollment_queue = '8G',
+
+    number_of_scoring_jobs = 10,
+    scoring_queue = '8G'
+    )

bob/bio/vein/configurations/grid/gpu.py

 #!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
 
-import facereclib
-
-# setup of the grid parameters
-# define a queue with demanding parameters
-grid = facereclib.utils.GridParameters(
-  training_queue = '8G',
-  # preprocessing
-  number_of_preprocessing_jobs = 32,
-  preprocessing_queue = '4G-io-big',
-  # feature extraction
-  number_of_extraction_jobs = 32,
-  extraction_queue = '4G-io-big',
-  # feature projection
-  number_of_projection_jobs = 32,
-  projection_queue = {},
-  # model enrollment
-  number_of_enrollment_jobs = 32,
-  enrollment_queue = {},
-  # scoring
-  number_of_scoring_jobs = 32,
-  scoring_queue = {'queue': 'q_gpu'},
-)
+import bob.bio.base.grid import Grid
 
+
+grid = Grid(
+    training_queue = '8G',
+
+    number_of_preprocessing_jobs = 32,
+    preprocessing_queue = '4G-io-big',
+
+    number_of_extraction_jobs = 32,
+    extraction_queue = '4G-io-big',
+
+    number_of_projection_jobs = 32,
+    projection_queue = {},
+
+    number_of_enrollment_jobs = 32,
+    enrollment_queue = {},
+
+    number_of_scoring_jobs = 32,
+    scoring_queue = {'queue': 'q_gpu'},
+    )

bob/bio/vein/configurations/grid/gpu2.py

 #!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
 
-import facereclib
-
-# setup of the grid parameters
-# define a queue with demanding parameters
-grid = facereclib.utils.GridParameters(
-  training_queue = '8G',
-  # preprocessing
-  number_of_preprocessing_jobs = 32,
-  preprocessing_queue = '4G-io-big',
-  # feature extraction
-  number_of_extraction_jobs = 32,
-  extraction_queue = '4G-io-big',
-  # feature projection
-  number_of_projection_jobs = 32,
-  projection_queue = {},
-  # model enrollment
-  number_of_enrollment_jobs = 32,
-  enrollment_queue = {},
-  # scoring
-  number_of_scoring_jobs = 32,
-  scoring_queue = '4G-io-big',
-)
+import bob.bio.base.grid import Grid
+
+
+grid = Grid(
+    training_queue = '8G',
+
+    number_of_preprocessing_jobs = 32,
+    preprocessing_queue = '4G-io-big',
+
+    number_of_extraction_jobs = 32,
+    extraction_queue = '4G-io-big',
+
+    number_of_projection_jobs = 32,
+    projection_queue = {},
+
+    number_of_enrollment_jobs = 32,
+    enrollment_queue = {},
+
+    number_of_scoring_jobs = 32,
+    scoring_queue = '4G-io-big',
+    )