From 331df5842f0113fbc776f37db7030128f102bd75 Mon Sep 17 00:00:00 2001
From: Andre Anjos <andre.anjos@idiap.ch>
Date: Fri, 8 Jul 2016 16:44:03 +0200
Subject: [PATCH] [extractors] Porting and cleanup
---
bob/bio/vein/__init__.py | 26 +--
.../vein/extractors/LocalBinaryPatterns.py | 101 ++++++-----
bob/bio/vein/extractors/MaximumCurvature.py | 163 +++++++++---------
.../extractors/NormalisedCrossCorrelation.py | 60 +++----
bob/bio/vein/extractors/PrincipalCurvature.py | 81 +++++----
.../vein/extractors/RepeatedLineTracking.py | 139 +++++++--------
bob/bio/vein/extractors/WideLineDetector.py | 89 +++++-----
bob/bio/vein/extractors/__init__.py | 10 --
bob/bio/vein/utils.py | 14 +-
9 files changed, 320 insertions(+), 363 deletions(-)
diff --git a/bob/bio/vein/__init__.py b/bob/bio/vein/__init__.py
index a60a22b..48ebe31 100644
--- a/bob/bio/vein/__init__.py
+++ b/bob/bio/vein/__init__.py
@@ -1,33 +1,13 @@
#!/usr/bin/env python
-# Pedro Tome <Pedro.Tome@idiap.ch>
-
-import configurations
-import tools
-import preprocessing
-import features
-import tests
-import script
-#import utils
-
+# vim: set fileencoding=utf-8 :
def get_config():
"""Returns a string containing the configuration information.
"""
- import pkg_resources
-
- packages = pkg_resources.require(__name__)
- this = packages[0]
- deps = packages[1:]
-
- retval = "%s: %s (%s)\n" % (this.key, this.version, this.location)
- retval += " - python dependencies:\n"
- for d in deps: retval += " - %s: %s (%s)\n" % (d.key, d.version, d.location)
-
- return retval.strip()
+ import bob.extension
+ return bob.extension.get_config(__name__)
# gets sphinx autodoc done right - don't remove it
__all__ = [_ for _ in dir() if not _.startswith('_')]
-
-
diff --git a/bob/bio/vein/extractors/LocalBinaryPatterns.py b/bob/bio/vein/extractors/LocalBinaryPatterns.py
index dd6e0db..20c08eb 100644
--- a/bob/bio/vein/extractors/LocalBinaryPatterns.py
+++ b/bob/bio/vein/extractors/LocalBinaryPatterns.py
@@ -5,18 +5,14 @@
import bob.ip.base
import bob.io.base
import numpy
-import math
-#from math import pi
-#from mumpy import sqrt
-import scipy.signal
-from facereclib.features.Extractor import Extractor
-from .. import utils
-#from facereclib.utils import histogram
+from bob.bio.base.features.Extractor import Extractor
+
class LocalBinaryPatterns (Extractor):
-
- """LBP feature extractor, paramters fixed based on
- L. Mirmohamadsadeghi and A. Drygajlo. Palm vein recognition uisng local texture patterns, IET Biometrics, pp. 1-9, 2013.
+ """LBP feature extractor
+
+ Parameters fixed based on L. Mirmohamadsadeghi and A. Drygajlo. Palm vein
+ recognition uisng local texture patterns, IET Biometrics, pp. 1-9, 2013.
"""
def __init__(
@@ -34,9 +30,8 @@ class LocalBinaryPatterns (Extractor):
lbp_add_average = False,
# histogram options
sparse_histogram = False,
- split_histogram = None
-
- ):
+ split_histogram = None,
+ ):
# call base class constructor
Extractor.__init__(
@@ -53,15 +48,15 @@ class LocalBinaryPatterns (Extractor):
lbp_compare_to_average = lbp_compare_to_average,
lbp_add_average = lbp_add_average,
sparse_histogram = sparse_histogram,
- split_histogram = split_histogram
- )
-
+ split_histogram = split_histogram,
+ )
+
# block parameters
self.m_block_size = block_size if isinstance(block_size, (tuple, list)) else (block_size, block_size)
self.m_block_overlap = block_overlap if isinstance(block_overlap, (tuple, list)) else (block_overlap, block_overlap)
if self.m_block_size[0] < self.m_block_overlap[0] or self.m_block_size[1] < self.m_block_overlap[1]:
raise ValueError("The overlap is bigger than the block size. This won't work. Please check your setup!")
-
+
self.m_lbp = bob.ip.base.LBP(
neighbors = lbp_neighbor_count,
radius = float(lbp_radius),
@@ -70,76 +65,78 @@ class LocalBinaryPatterns (Extractor):
add_average_bit = lbp_add_average,
uniform = lbp_uniform,
rotation_invariant = lbp_rotation_invariant,
- border_handling = 'wrap'
- )
+ border_handling = 'wrap',
+ )
self.m_split = split_histogram
self.m_sparse = sparse_histogram
if self.m_sparse and self.m_split:
- raise ValueError("Sparse histograms cannot be split! Check your setup!")
+ raise ValueError("Sparse histograms cannot be split! Check your setup.")
def __fill__(self, lbphs_array, lbphs_blocks, j):
"""Copies the given array into the given blocks"""
+
# fill array in the desired shape
- #For debugging
- #import ipdb; ipdb.set_trace()
+ #For debugging
+ #import ipdb; ipdb.set_trace()
for b in range(self.m_n_blocks):
lbphs_array[b * self.m_n_bins : (b+1) * self.m_n_bins] = lbphs_blocks[b][:]
-
+
def lbp_features(self, finger_image, mask):
- """Computes and returns the LBP features for the given input fingervein image"""
-
- # For debugging
- #import ipdb; ipdb.set_trace()
-
+ """Computes and returns the LBP features for the given input fingervein
+ image"""
+
+ # For debugging
+ #import ipdb; ipdb.set_trace()
+
finger_image = finger_image.astype(numpy.float64)
finger_mask = numpy.zeros(mask.shape)
- finger_mask[mask == True] = 1
-
+ finger_mask[mask == True] = 1
+
# Mask the vein image with the finger region
- finger_image = finger_image*finger_mask
-
+ finger_image = finger_image*finger_mask
+
# Computes LBP histograms
abs_blocks = bob.ip.base.lbphs(finger_image, self.m_lbp, self.m_block_size, self.m_block_overlap)
# Converts to Blitz array (of different dimensionalities)
self.m_n_bins = abs_blocks.shape[1]
self.m_n_blocks = abs_blocks.shape[0]
-
+
shape = self.m_n_bins * self.m_n_blocks
-
- # create new array
+
+ # create new array
lbphs_array = numpy.zeros(shape, 'float64')
-
- #For debugging
- #import ipdb; ipdb.set_trace()
-
+
+ #For debugging
+ #import ipdb; ipdb.set_trace()
+
# fill the array with the absolute values of the Gabor wavelet transform
self.__fill__(lbphs_array, abs_blocks, 0)
-
+
# return the concatenated list of all histograms
return lbphs_array
-
-
- def __call__(self, image):
+
+
+ def __call__(self, image):
"""Reads the input image, extract the features based on LBP of the fingervein image, and writes the resulting template"""
- #For debugging
-
+ #For debugging
+
finger_image = image[0] #Normalized image with histogram equalization
- finger_mask = image[1]
-
- return self.lbp_features(finger_image, finger_mask)
-
-
+ finger_mask = image[1]
+
+ return self.lbp_features(finger_image, finger_mask)
+
+
def save_feature(self, feature, feature_file):
f = bob.io.base.HDF5File(feature_file, 'w')
f.set('feature', feature)
-
+
def read_feature(self, feature_file):
f = bob.io.base.HDF5File(feature_file, 'r')
image = f.read('feature')
- return (image)
\ No newline at end of file
+ return image
diff --git a/bob/bio/vein/extractors/MaximumCurvature.py b/bob/bio/vein/extractors/MaximumCurvature.py
index e3b1958..08d3b2c 100644
--- a/bob/bio/vein/extractors/MaximumCurvature.py
+++ b/bob/bio/vein/extractors/MaximumCurvature.py
@@ -2,25 +2,26 @@
# vim: set fileencoding=utf-8 :
# Pedro Tome <Pedro.Tome@idiap.ch>
+import math
+import numpy
+
import bob.core
import bob.io.base
-import numpy
-import math
-#from math import pi
-#from mumpy import sqrt
-import scipy.signal
-from facereclib.features.Extractor import Extractor
+from bob.bio.base.features.Extractor import Extractor
+
from .. import utils
+
class MaximumCurvature (Extractor):
-
- """MiuraMax feature extractor based on
- N. Miura, A. Nagasaka, and T. Miyatake, Extraction of Finger-Vein Pattern Using Maximum Curvature Points in Image Profiles.
- Proceedings on IAPR conference on machine vision applications, 9 (2005), pp. 347--350
+ """MiuraMax feature extractor
+
+ Based on N. Miura, A. Nagasaka, and T. Miyatake, Extraction of Finger-Vein
+ Pattern Using Maximum Curvature Points in Image Profiles. Proceedings on IAPR
+ conference on machine vision applications, 9 (2005), pp. 347--350
"""
-
-
+
+
def __init__(
self,
sigma = 5, #Sigma used for determining derivatives
@@ -33,65 +34,66 @@ class MaximumCurvature (Extractor):
sigma = sigma,
gpu = gpu
)
-
+
# block parameters
self.sigma = sigma
self.gpu = gpu
-
-
- def maximum_curvature(self, image, mask):
- """Computes and returns the Maximum Curvature features for the given input fingervein image"""
-
+
+
+ def maximum_curvature(self, image, mask):
+ """Computes and returns the Maximum Curvature features for the given input
+ fingervein image"""
+
if image.dtype != numpy.uint8:
image = bob.core.convert(image,numpy.uint8,(0,255),(0,1))
#No es necesario pasarlo a uint8, en matlab lo dejan en float64. Comprobar si varian los resultados en vera database y ajustar.
finger_mask = numpy.zeros(mask.shape)
- finger_mask[mask == True] = 1
-
+ finger_mask[mask == True] = 1
+
winsize = numpy.ceil(4*self.sigma)
-
+
x = numpy.arange(-winsize, winsize+1)
y = numpy.arange(-winsize, winsize+1)
X, Y = numpy.meshgrid(x, y)
-
+
h = (1/(2*math.pi*self.sigma**2))*numpy.exp(-(X**2 + Y**2)/(2*self.sigma**2))
hx = (-X/(self.sigma**2))*h
hxx = ((X**2 - self.sigma**2)/(self.sigma**4))*h
hy = hx.T
hyy = hxx.T
hxy = ((X*Y)/(self.sigma**4))*h
-
+
# Do the actual filtering
-
+
fx = utils.imfilter(image, hx, self.gpu, conv=False)
fxx = utils.imfilter(image, hxx, self.gpu, conv=False)
fy = utils.imfilter(image, hy, self.gpu, conv=False)
fyy = utils.imfilter(image, hyy, self.gpu, conv=False)
fxy = utils.imfilter(image, hxy, self.gpu, conv=False)
-
+
f1 = 0.5*numpy.sqrt(2)*(fx + fy) # \ #
f2 = 0.5*numpy.sqrt(2)*(fx - fy) # / #
f11 = 0.5*fxx + fxy + 0.5*fyy # \\ #
f22 = 0.5*fxx - fxy + 0.5*fyy # // #
-
+
img_h, img_w = image.shape #Image height and width
-
+
# Calculate curvatures
k = numpy.zeros((img_h, img_w, 4))
k[:,:,0] = (fxx/((1 + fx**2)**(3/2)))*finger_mask # hor #
k[:,:,1] = (fyy/((1 + fy**2)**(3/2)))*finger_mask # ver #
k[:,:,2] = (f11/((1 + f1**2)**(3/2)))*finger_mask # \ #
k[:,:,3] = (f22/((1 + f2**2)**(3/2)))*finger_mask # / #
-
+
# Scores
Vt = numpy.zeros(image.shape)
Wr = 0
-
+
# Horizontal direction
bla = k[:,:,0] > 0
- for y in range(0,img_h):
- for x in range(0,img_w):
+ for y in range(0,img_h):
+ for x in range(0,img_w):
if (bla[y,x]):
Wr = Wr + 1
if ( Wr > 0 and (x == (img_w-1) or not bla[y,x]) ):
@@ -100,23 +102,23 @@ class MaximumCurvature (Extractor):
pos_end = x
else:
pos_end = x - 1
-
- pos_start = pos_end - Wr + 1 # Start pos of concave
+
+ pos_start = pos_end - Wr + 1 # Start pos of concave
if (pos_start == pos_end):
I=numpy.argmax(k[y,pos_start,0])
- else:
+ else:
I=numpy.argmax(k[y,pos_start:pos_end+1,0])
-
+
pos_max = pos_start + I
Scr = k[y,pos_max,0]*Wr
Vt[y,pos_max] = Vt[y,pos_max] + Scr
- Wr = 0
-
+ Wr = 0
+
# Vertical direction
bla = k[:,:,1] > 0
- for x in range(0,img_w):
- for y in range(0,img_h):
+ for x in range(0,img_w):
+ for y in range(0,img_h):
if (bla[y,x]):
Wr = Wr + 1
if ( Wr > 0 and (y == (img_h-1) or not bla[y,x]) ):
@@ -124,20 +126,20 @@ class MaximumCurvature (Extractor):
# Reached edge of image
pos_end = y
else:
- pos_end = y - 1
-
+ pos_end = y - 1
+
pos_start = pos_end - Wr + 1 # Start pos of concave
if (pos_start == pos_end):
I=numpy.argmax(k[pos_start,x,1])
else:
I=numpy.argmax(k[pos_start:pos_end+1,x,1])
-
- pos_max = pos_start + I
+
+ pos_max = pos_start + I
Scr = k[pos_max,x,1]*Wr
-
+
Vt[pos_max,x] = Vt[pos_max,x] + Scr
Wr = 0
-
+
# Direction: \ #
bla = k[:,:,2] > 0
for start in range(0,img_w+img_h-1):
@@ -149,11 +151,11 @@ class MaximumCurvature (Extractor):
x = 0
y = start - img_w + 1
done = False
-
+
while (not done):
if(bla[y,x]):
Wr = Wr + 1
-
+
if ( Wr > 0 and (y == img_h-1 or x == img_w-1 or not bla[y,x]) ):
if (y == img_h-1 or x == img_w-1):
# Reached edge of image
@@ -162,10 +164,10 @@ class MaximumCurvature (Extractor):
else:
pos_x_end = x - 1
pos_y_end = y - 1
-
+
pos_x_start = pos_x_end - Wr + 1
pos_y_start = pos_y_end - Wr + 1
-
+
if (pos_y_start == pos_y_end and pos_x_start == pos_x_end):
d = k[pos_y_start, pos_x_start, 2]
elif (pos_y_start == pos_y_end):
@@ -174,23 +176,23 @@ class MaximumCurvature (Extractor):
d = numpy.diag(k[pos_y_start:pos_y_end+1, pos_x_start, 2])
else:
d = numpy.diag(k[pos_y_start:pos_y_end+1, pos_x_start:pos_x_end+1, 2])
-
- I = numpy.argmax(d)
-
- pos_x_max = pos_x_start + I
- pos_y_max = pos_y_start + I
-
+
+ I = numpy.argmax(d)
+
+ pos_x_max = pos_x_start + I
+ pos_y_max = pos_y_start + I
+
Scr = k[pos_y_max,pos_x_max,2]*Wr
-
+
Vt[pos_y_max,pos_x_max] = Vt[pos_y_max,pos_x_max] + Scr
Wr = 0
-
+
if((x == img_w-1) or (y == img_h-1)):
done = True
else:
x = x + 1
y = y + 1
-
+
# Direction: /
bla = k[:,:,3] > 0
for start in range(0,img_w+img_h-1):
@@ -202,7 +204,7 @@ class MaximumCurvature (Extractor):
x = 0
y = img_w+img_h-start-1
done = False
-
+
while (not done):
if(bla[y,x]):
Wr = Wr + 1
@@ -214,10 +216,10 @@ class MaximumCurvature (Extractor):
else:
pos_x_end = x - 1
pos_y_end = y + 1
-
+
pos_x_start = pos_x_end - Wr + 1
pos_y_start = pos_y_end + Wr - 1
-
+
if (pos_y_start == pos_y_end and pos_x_start == pos_x_end):
d = k[pos_y_end, pos_x_start, 3]
elif (pos_y_start == pos_y_end):
@@ -226,19 +228,19 @@ class MaximumCurvature (Extractor):
d = numpy.diag(numpy.flipud(k[pos_y_end:pos_y_start+1, pos_x_start, 3]))
else:
d = numpy.diag(numpy.flipud(k[pos_y_end:pos_y_start+1, pos_x_start:pos_x_end+1, 3]))
-
- I = numpy.argmax(d)
- pos_x_max = pos_x_start + I
- pos_y_max = pos_y_start - I
+
+ I = numpy.argmax(d)
+ pos_x_max = pos_x_start + I
+ pos_y_max = pos_y_start - I
Scr = k[pos_y_max,pos_x_max,3]*Wr
Vt[pos_y_max,pos_x_max] = Vt[pos_y_max,pos_x_max] + Scr
Wr = 0
-
+
if((x == img_w-1) or (y == 0)):
done = True
else:
x = x + 1
- y = y - 1
+ y = y - 1
## Connection of vein centres
Cd = numpy.zeros((img_h, img_w, 4))
@@ -248,34 +250,31 @@ class MaximumCurvature (Extractor):
Cd[y,x,1] = min(numpy.amax(Vt[y+1:y+3,x]), numpy.amax(Vt[y-2:y,x])) # Vert #
Cd[y,x,2] = min(numpy.amax(Vt[y-2:y,x-2:x]), numpy.amax(Vt[y+1:y+3,x+1:x+3])) # \ #
Cd[y,x,3] = min(numpy.amax(Vt[y+1:y+3,x-2:x]), numpy.amax(Vt[y-2:y,x+1:x+3])) # / #
-
+
#Veins
img_veins = numpy.amax(Cd,axis=2)
-
+
# Binarise the vein image
md = numpy.median(img_veins[img_veins>0])
img_veins_bin = img_veins > md
return img_veins_bin.astype(numpy.float64)
-
-
- def __call__(self, image):
+
+
+ def __call__(self, image):
"""Reads the input image, extract the features based on Maximum Curvature of the fingervein image, and writes the resulting template"""
-
+
finger_image = image[0] #Normalized image with or without histogram equalization
- finger_mask = image[1]
-
- return self.maximum_curvature(finger_image, finger_mask)
-
+ finger_mask = image[1]
+
+ return self.maximum_curvature(finger_image, finger_mask)
+
def save_feature(self, feature, feature_file):
f = bob.io.base.HDF5File(feature_file, 'w')
f.set('feature', feature)
-
+
def read_feature(self, feature_file):
f = bob.io.base.HDF5File(feature_file, 'r')
image = f.read('feature')
- return (image)
-
-
-
\ No newline at end of file
+ return image
diff --git a/bob/bio/vein/extractors/NormalisedCrossCorrelation.py b/bob/bio/vein/extractors/NormalisedCrossCorrelation.py
index 1bb7250..75a9415 100644
--- a/bob/bio/vein/extractors/NormalisedCrossCorrelation.py
+++ b/bob/bio/vein/extractors/NormalisedCrossCorrelation.py
@@ -2,48 +2,44 @@
# vim: set fileencoding=utf-8 :
# Pedro Tome <Pedro.Tome@idiap.ch>
-import bob.core
+import numpy
+
import bob.io.base
-import numpy
-from facereclib.features.Extractor import Extractor
+from bob.bio.base.features.Extractor import Extractor
+
class NormalisedCrossCorrelation (Extractor):
-
- """Normalised Cross-Correlation feature extractor based on
- M. Kono, H. Ueki, and S.Umemura. Near-infrared finger vein patterns for personal
- identification. Appl. Opt. 41(35):7429-7436, 2002
+ """Normalised Cross-Correlation feature extractor
+
+ Based on M. Kono, H. Ueki, and S.Umemura. Near-infrared finger vein patterns
+ for personal identification. Appl. Opt. 41(35):7429-7436, 2002
"""
- def __init__(
- self,
-
- ):
-
- # call base class constructor
- Extractor.__init__(
- self,
-
- )
-
- # block parameters
-
- def __call__(self, image, mask):
- """Reads the input image, extract the features based on Normalised Cross-Correlation of the fingervein image, and writes the resulting template"""
-
+ def __init__(self):
+ Extractor.__init__(self)
+
+
+ def __call__(self, image, mask):
+ """Reads the input image, extract the features based on Normalised
+ Cross-Correlation of the fingervein image, and writes the resulting
+ template"""
+
finger_image = image #Normalized image with histogram equalization
- finger_mask = mask
-
- image_vein = finger_image*finger_mask
-
- #TODO
-
- return image_vein.astype(numpy.float64)
-
+ finger_mask = mask
+
+ image_vein = finger_image*finger_mask
+
+ #TODO
+
+ return image_vein.astype(numpy.float64)
+
+
def save_feature(self, feature, feature_file):
f = bob.io.base.HDF5File(feature_file, 'w')
f.set('feature', feature)
-
+
+
def read_feature(self, feature_file):
f = bob.io.base.HDF5File(feature_file, 'r')
image = f.read('feature')
diff --git a/bob/bio/vein/extractors/PrincipalCurvature.py b/bob/bio/vein/extractors/PrincipalCurvature.py
index 88de36c..5ea46c2 100644
--- a/bob/bio/vein/extractors/PrincipalCurvature.py
+++ b/bob/bio/vein/extractors/PrincipalCurvature.py
@@ -4,48 +4,46 @@
import bob.io.base
import numpy
-import math
-#from math import pi
-#from mumpy import sqrt
-import scipy.signal
-from facereclib.features.Extractor import Extractor
-from .. import utils
+
+
+from bob.bio.base.features.Extractor import Extractor
class MaximumCurvature (Extractor):
-
- """MiuraMax feature extractor based on
- J.H. Choi, W. Song, T. Kim, S.R. Lee and H.C. Kim, Finger vein extraction using gradient normalization and principal curvature.
- Proceedings on Image Processing: Machine Vision Applications II, SPIE 7251, (2009)
+ """MiuraMax feature extractor
+
+ Based on J.H. Choi, W. Song, T. Kim, S.R. Lee and H.C. Kim, Finger vein
+ extraction using gradient normalization and principal curvature. Proceedings
+ on Image Processing: Machine Vision Applications II, SPIE 7251, (2009)
"""
-
+
def __init__(
self,
sigma = 2, # Gaussian standard deviation applied
- threshold = 1.3, # Percentage of maximum used for hard thresholding
- gpu = False
-
- ):
+ threshold = 1.3, # Percentage of maximum used for hard thresholding
+ gpu = False,
+ ):
# call base class constructor
Extractor.__init__(
self,
sigma = sigma,
threshold = threshold,
- gpu = gpu
- )
-
+ gpu = gpu,
+ )
+
# block parameters
self.sigma = sigma
self.threshold = threshold
self.gpu = gpu
-
-
- def principal_curvature(self, image, mask):
- """Computes and returns the Maximum Curvature features for the given input fingervein image"""
-
+
+
+ def principal_curvature(self, image, mask):
+ """Computes and returns the Maximum Curvature features for the given input
+ fingervein image"""
+
finger_mask = numpy.zeros(mask.shape)
- finger_mask[mask == True] = 1
-
+ finger_mask[mask == True] = 1
+
sigma = numpy.sqrt(self.sigma**2/2)
gx = ut_gauss(img,sigma,1,0)
@@ -55,9 +53,9 @@ class MaximumCurvature (Extractor):
# Apply threshold
gamma = (self.threshold/100)*max(max(Gmag))
-
+
indices = find(Gmag < gamma)
-
+
gx[indices] = 0
gy[indices] = 0
@@ -65,7 +63,7 @@ class MaximumCurvature (Extractor):
Gmag( find(Gmag == 0) ) = 1 # Avoid dividing by zero
gx = gx/Gmag
gy = gy/Gmag
-
+
hxx = ut_gauss(gx,sigma,1,0)
hxy = ut_gauss(gx,sigma,0,1)
hyy = ut_gauss(gy,sigma,0,1)
@@ -80,31 +78,30 @@ class MaximumCurvature (Extractor):
veins = veins*finger_mask
-
+
# Binarise the vein image by otsu
md = numpy.median(img_veins[img_veins>0])
img_veins_bin = img_veins > md
return img_veins_bin.astype(numpy.float64)
-
-
- def __call__(self, image):
- """Reads the input image, extract the features based on Principal Curvature of the fingervein image, and writes the resulting template"""
-
+
+
+ def __call__(self, image):
+ """Reads the input image, extract the features based on Principal Curvature
+ of the fingervein image, and writes the resulting template"""
+
finger_image = image[0] #Normalized image with or without histogram equalization
- finger_mask = image[1]
-
- return self.principal_curvature(finger_image, finger_mask)
-
+ finger_mask = image[1]
+
+ return self.principal_curvature(finger_image, finger_mask)
+
def save_feature(self, feature, feature_file):
f = bob.io.base.HDF5File(feature_file, 'w')
f.set('feature', feature)
-
+
+
def read_feature(self, feature_file):
f = bob.io.base.HDF5File(feature_file, 'r')
image = f.read('feature')
return (image)
-
-
-
\ No newline at end of file
diff --git a/bob/bio/vein/extractors/RepeatedLineTracking.py b/bob/bio/vein/extractors/RepeatedLineTracking.py
index e559c8b..8dce5ec 100644
--- a/bob/bio/vein/extractors/RepeatedLineTracking.py
+++ b/bob/bio/vein/extractors/RepeatedLineTracking.py
@@ -2,23 +2,23 @@
# vim: set fileencoding=utf-8 :
# Pedro Tome <Pedro.Tome@idiap.ch>
+import numpy
+import math
+
import bob.core
import bob.io.base
import bob.ip.base
-import numpy
-import math
-#from math import pi
-#from mumpy import sqrt
-import scipy.signal
-from facereclib.features.Extractor import Extractor
-from .. import utils
+from bob.bio.base.features.Extractor import Extractor
+
class RepeatedLineTracking (Extractor):
-
- """Repeated Line Tracking feature extractor 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
+ """Repeated Line Tracking feature extractor
+
+ 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__(
@@ -26,18 +26,18 @@ class RepeatedLineTracking (Extractor):
iterations = 3000, # Maximum number of iterations
r = 1, # Distance between tracking point and cross section of the profile
profile_w = 21, # Width of profile (Error: profile_w must be odd)
- rescale = True
- ):
-
+ rescale = True,
+ ):
+
# call base class constructor
Extractor.__init__(
self,
iterations = iterations,
r = r,
profile_w = profile_w,
- rescale = rescale
- )
-
+ rescale = rescale,
+ )
+
# block parameters
self.iterations = iterations
self.r = r
@@ -46,23 +46,24 @@ class RepeatedLineTracking (Extractor):
def repeated_line_tracking(self, finger_image, mask):
- """Computes and returns the MiuraMax features for the given input fingervein image"""
-
+ """Computes and returns the MiuraMax features for the given input
+ fingervein image"""
+
#Convert image to uint8
if finger_image.dtype != numpy.uint8:
finger_image = bob.core.convert(finger_image,numpy.uint8,(0,255),(0,1))
finger_mask = numpy.zeros(mask.shape)
- finger_mask[mask == True] = 1
-
+ finger_mask[mask == True] = 1
+
# Rescale image if required
if self.rescale == True:
- scaling_factor = 0.6
+ scaling_factor = 0.6
finger_image = bob.ip.base.scale(finger_image,scaling_factor)
finger_mask = bob.ip.base.scale(finger_mask,scaling_factor)
#To eliminate residuals from the scalation of the binary mask
finger_mask = scipy.ndimage.binary_dilation(finger_mask, structure=numpy.ones((1,1))).astype(int)
-
+
p_lr = 0.5 # Probability of goin left or right
p_ud = 0.25 # Probability of going up or down
@@ -72,24 +73,24 @@ class RepeatedLineTracking (Extractor):
# Check if progile w is even
if (self.profile_w.__mod__(2) == 0):
print ('Error: profile_w must be odd')
-
+
ro = numpy.round(self.r*math.sqrt(2)/2) # r for oblique directions
hW = (self.profile_w-1)/2 # half width for horz. and vert. directions
hWo = numpy.round(hW*math.sqrt(2)/2) # half width for oblique directions
-
+
# Omit unreachable borders
finger_mask[0:self.r+hW,:] = 0
finger_mask[finger_mask.shape[0]-(self.r+hW):,:] = 0
finger_mask[:,0:self.r+hW] = 0
finger_mask[:,finger_mask.shape[1]-(self.r+hW):] = 0
-
+
## Uniformly distributed starting points
aux = numpy.argwhere( (finger_mask > 0) == True )
indices = numpy.random.permutation(aux)
indices = indices[0:self.iterations,:] # Limit to number of iterations
- ## Iterate through all starting points
- for it in range(0,self.iterations):
+ ## Iterate through all starting points
+ for it in range(0,self.iterations):
yc = indices[it,0] # Current tracking point, y
xc = indices[it,1] # Current tracking point, x
@@ -98,17 +99,17 @@ class RepeatedLineTracking (Extractor):
if (numpy.random.random_sample() >= 0.5):
Dlr = -1 # Going left
else:
- Dlr = 1 # Going right
-
+ Dlr = 1 # Going right
+
# Going up or down ?
if (numpy.random.random_sample() >= 0.5):
Dud = -1 # Going up
else:
- Dud = 1 # Going down
-
+ Dud = 1 # Going down
+
# Initialize locus-positition table Tc
Tc = numpy.zeros(finger_image.shape, numpy.bool)
-
+
#Dlr = -1; Dud=-1; LET OP
Vl = 1
while (Vl > 0):
@@ -116,27 +117,27 @@ class RepeatedLineTracking (Extractor):
Nr = numpy.zeros([3,3], numpy.bool)
Rnd = numpy.random.random_sample()
#Rnd = 0.8 LET OP
- if (Rnd < p_lr):
+ if (Rnd < p_lr):
# Going left or right
Nr[:,1+Dlr] = True
- elif (Rnd >= p_lr) and (Rnd < (p_lr + p_ud)):
+ elif (Rnd >= p_lr) and (Rnd < (p_lr + p_ud)):
# Going up or down
Nr[1+Dud,:] = True
- else:
+ else:
# Going any direction
Nr = numpy.ones([3,3], numpy.bool)
Nr[1,1] = False
- #tmp = numpy.argwhere( (~Tc[yc-2:yc+1,xc-2:xc+1] & Nr & finger_mask[yc-2:yc+1,xc-2:xc+1].astype(numpy.bool)).T.reshape(-1) == True )
+ #tmp = numpy.argwhere( (~Tc[yc-2:yc+1,xc-2:xc+1] & Nr & finger_mask[yc-2:yc+1,xc-2:xc+1].astype(numpy.bool)).T.reshape(-1) == True )
tmp = numpy.argwhere( (~Tc[yc-1:yc+2,xc-1:xc+2] & Nr & finger_mask[yc-1:yc+2,xc-1:xc+2].astype(numpy.bool)).T.reshape(-1) == True )
Nc = numpy.concatenate((xc + filtermask[tmp,0],yc + filtermask[tmp,1]),axis=1)
if (Nc.size==0):
Vl=-1
continue
-
+
## Detect dark line direction near current tracking point
Vdepths = numpy.zeros((Nc.shape[0],1)) # Valley depths
- for i in range(0,Nc.shape[0]):
- ## Horizontal or vertical
+ for i in range(0,Nc.shape[0]):
+ ## Horizontal or vertical
if (Nc[i,1] == yc):
# Horizontal plane
yp = Nc[i,1]
@@ -157,7 +158,7 @@ class RepeatedLineTracking (Extractor):
# Up direction
yp = Nc[i,1] - self.r
Vdepths[i] = finger_image[yp, xp + hW] - 2*finger_image[yp,xp] + finger_image[yp, xp - hW]
-
+
## Oblique directions
if ( (Nc[i,0] > xc) and (Nc[i,1] < yc) ) or ( (Nc[i,0] < xc) and (Nc[i,1] > yc) ):
# Diagonal, up /
@@ -184,70 +185,70 @@ class RepeatedLineTracking (Extractor):
# End search of candidates
index = numpy.argmax(Vdepths) #Determine best candidate
# Register tracking information
- Tc[yc, xc] = True
+ Tc[yc, xc] = True
# Increase value of tracking space
Tr[yc, xc] = Tr[yc, xc] + 1
# Move tracking point
xc = Nc[index, 0]
yc = Nc[index, 1]
-
+
img_veins = Tr
-
+
# Binarise the vein image
md = numpy.median(img_veins[img_veins>0])
img_veins_bin = img_veins > md
img_veins_bin = scipy.ndimage.binary_closing(img_veins_bin, structure=numpy.ones((2,2))).astype(int)
- #import ipdb; ipdb.set_trace()
+ #import ipdb; ipdb.set_trace()
#img_veins_bin2 = scipy.ndimage.binary_closing(img_veins_bin, structure=numpy.ones((2,2))).astype(int)
#from PIL import Image
-
+
#Image.fromarray(bob.core.convert(img_veins_bin,numpy.uint8,(0,255),(0,1))).show()
#skel = self.skeletonize(img_veins_bin2)
#Image.fromarray(bob.core.convert(skel,numpy.uint8,(0,255),(0,1))).show()
- return img_veins_bin.astype(numpy.float64)
-
+ return img_veins_bin.astype(numpy.float64)
+
+
def skeletonize(self, img):
import scipy.ndimage.morphology as m
- h1 = numpy.array([[0, 0, 0],[0, 1, 0],[1, 1, 1]])
- m1 = numpy.array([[1, 1, 1],[0, 0, 0],[0, 0, 0]])
- h2 = numpy.array([[0, 0, 0],[1, 1, 0],[0, 1, 0]])
- m2 = numpy.array([[0, 1, 1],[0, 0, 1],[0, 0, 0]])
- hit_list = []
+ h1 = numpy.array([[0, 0, 0],[0, 1, 0],[1, 1, 1]])
+ m1 = numpy.array([[1, 1, 1],[0, 0, 0],[0, 0, 0]])
+ h2 = numpy.array([[0, 0, 0],[1, 1, 0],[0, 1, 0]])
+ m2 = numpy.array([[0, 1, 1],[0, 0, 1],[0, 0, 0]])
+ hit_list = []
miss_list = []
- for k in range(4):
+ for k in range(4):
hit_list.append(numpy.rot90(h1, k))
hit_list.append(numpy.rot90(h2, k))
miss_list.append(numpy.rot90(m1, k))
- miss_list.append(numpy.rot90(m2, k))
+ miss_list.append(numpy.rot90(m2, k))
img = img.copy()
while True:
last = img
- for hit, miss in zip(hit_list, miss_list):
- hm = m.binary_hit_or_miss(img, hit, miss)
- img = numpy.logical_and(img, numpy.logical_not(hm))
- if numpy.all(img == last):
+ for hit, miss in zip(hit_list, miss_list):
+ hm = m.binary_hit_or_miss(img, hit, miss)
+ img = numpy.logical_and(img, numpy.logical_not(hm))
+ if numpy.all(img == last):
break
return img
- def __call__(self, image):
- """Reads the input image, extract the features based on Maximum Curvature of the fingervein image, and writes the resulting template"""
-
+ def __call__(self, image):
+ """Reads the input image, extract the features based on Maximum Curvature
+ of the fingervein image, and writes the resulting template"""
+
finger_image = image[0] #Normalized image with or without histogram equalization
- finger_mask = image[1]
-
- return self.repeated_line_tracking(finger_image, finger_mask)
-
+ finger_mask = image[1]
+
+ return self.repeated_line_tracking(finger_image, finger_mask)
+
def save_feature(self, feature, feature_file):
f = bob.io.base.HDF5File(feature_file, 'w')
f.set('feature', feature)
-
+
def read_feature(self, feature_file):
f = bob.io.base.HDF5File(feature_file, 'r')
image = f.read('feature')
return (image)
-
-
\ No newline at end of file
diff --git a/bob/bio/vein/extractors/WideLineDetector.py b/bob/bio/vein/extractors/WideLineDetector.py
index 26d6e06..505e891 100644
--- a/bob/bio/vein/extractors/WideLineDetector.py
+++ b/bob/bio/vein/extractors/WideLineDetector.py
@@ -2,24 +2,23 @@
# vim: set fileencoding=utf-8 :
# Pedro Tome <Pedro.Tome@idiap.ch>
-import bob.core
import bob.io.base
import bob.ip.base
import numpy
-import math
-#from math import pi
-#from mumpy import sqrt
-import scipy.signal
+import scipy
import scipy.misc
-from facereclib.features.Extractor import Extractor
-from .. import utils
+
+from bob.bio.base.features.Extractor import Extractor
+
class WideLineDetector (Extractor):
-
- """Wide Line Detector feature extractor based on
- B. Huang, Y. Dai, R. Li, D. Tang and W. Li. Finger-vein authentication based on wide line detector and pattern
- normalization, Proceedings on 20th International Conference on Pattern Recognition (ICPR), 2010
+ """Wide Line Detector feature extractor
+
+ Based on B. Huang, Y. Dai, R. Li, D. Tang and W. Li. Finger-vein
+ authentication based on wide line detector and pattern normalization,
+ Proceedings on 20th International Conference on Pattern Recognition (ICPR),
+ 2010.
"""
def __init__(
@@ -27,9 +26,8 @@ class WideLineDetector (Extractor):
radius = 5, #Radius of the circular neighbourhood region
threshold = 1, #Neigborhood threshold
g = 41, #Sum of neigbourhood threshold
- rescale = True
-
- ):
+ rescale = True,
+ ):
# call base class constructor
Extractor.__init__(
@@ -37,9 +35,9 @@ class WideLineDetector (Extractor):
radius = radius,
threshold = threshold,
g = g,
- rescale = rescale
- )
-
+ rescale = rescale,
+ )
+
# block parameters
self.radius = radius
self.threshold = threshold
@@ -48,13 +46,14 @@ class WideLineDetector (Extractor):
def wide_line_detector(self, finger_image, mask):
- """Computes and returns the Wide Line Detector features for the given input fingervein image"""
-
- finger_image = finger_image.astype(numpy.float64)
+ """Computes and returns the Wide Line Detector features for the given input
+ fingervein image"""
+
+ finger_image = finger_image.astype(numpy.float64)
finger_mask = numpy.zeros(mask.shape)
- finger_mask[mask == True] = 1
-
+ finger_mask[mask == True] = 1
+
# Rescale image if required
if self.rescale == True:
scaling_factor = 0.24
@@ -68,40 +67,42 @@ class WideLineDetector (Extractor):
x = numpy.arange((-1)*self.radius, self.radius+1)
y = numpy.arange((-1)*self.radius, self.radius+1)
X, Y = numpy.meshgrid(x,y)
-
+
N = X**2 + Y**2 <= self.radius**2 # Neighbourhood mask
-
- img_h, img_w = finger_image.shape #Image height and width
-
+
+ img_h, img_w = finger_image.shape #Image height and width
+
veins = numpy.zeros(finger_image.shape)
-
- for y in range(self.radius,img_h-self.radius):
- for x in range(self.radius,img_w-self.radius):
+
+ for y in range(self.radius,img_h-self.radius):
+ for x in range(self.radius,img_w-self.radius):
s=((finger_image[y-self.radius:y+self.radius+1,x-self.radius:x+self.radius+1] - finger_image[y,x]) <= self.threshold)
m = (s*N).sum()
veins[y,x] = float(m <= self.g)
-
+
# Mask the vein image with the finger region
img_veins_bin = veins*finger_mask
-
+
return img_veins_bin
-
-
- def __call__(self, image):
- """Reads the input image, extract the features based on Wide Line Detector of the fingervein image, and writes the resulting template"""
- #For debugging
-
+
+
+ def __call__(self, image):
+ """Reads the input image, extract the features based on Wide Line Detector
+ of the fingervein image, and writes the resulting template"""
+ #For debugging
+
finger_image = image[0] #Normalized image with histogram equalization
- finger_mask = image[1]
-
- return self.wide_line_detector(finger_image, finger_mask)
-
-
+ finger_mask = image[1]
+
+ return self.wide_line_detector(finger_image, finger_mask)
+
+
def save_feature(self, feature, feature_file):
f = bob.io.base.HDF5File(feature_file, 'w')
f.set('feature', feature)
-
+
+
def read_feature(self, feature_file):
f = bob.io.base.HDF5File(feature_file, 'r')
image = f.read('feature')
- return (image)
\ No newline at end of file
+ return (image)
diff --git a/bob/bio/vein/extractors/__init__.py b/bob/bio/vein/extractors/__init__.py
index 596d0be..e69de29 100644
--- a/bob/bio/vein/extractors/__init__.py
+++ b/bob/bio/vein/extractors/__init__.py
@@ -1,10 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-
-"""Feature Extraction"""
-
-from NormalisedCrossCorrelation import NormalisedCrossCorrelation
-from MaximumCurvature import MaximumCurvature
-from RepeatedLineTracking import RepeatedLineTracking
-from WideLineDetector import WideLineDetector
-from LocalBinaryPatterns import LocalBinaryPatterns
diff --git a/bob/bio/vein/utils.py b/bob/bio/vein/utils.py
index 3c75c1a..7e84f45 100644
--- a/bob/bio/vein/utils.py
+++ b/bob/bio/vein/utils.py
@@ -1,9 +1,7 @@
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
-# Pedro Tome <Pedro.Tome@idiap.ch>
-import numpy.random
-import scipy.ndimage
+import numpy
import scipy.signal
import bob.ip.base
import bob.sp
@@ -12,23 +10,21 @@ import bob.core
def imfilter(a, b, gpu=False, conv=True):
"""imfilter function based on MATLAB implementation."""
+
if (a.dtype == numpy.uint8):
- a= bob.core.convert(a,numpy.float64,(0,1))
+ a= bob.core.convert(a,numpy.float64,(0,1))
M, N = a.shape
if conv == True:
- b = bob.ip.base.rotate(b, 180)
+ b = bob.ip.base.rotate(b, 180)
shape = numpy.array((0,0))
shape[0] = a.shape[0] + b.shape[0] - 1
shape[1] = a.shape[1] + b.shape[1] - 1
a_ext = numpy.ndarray(shape=shape, dtype=numpy.float64)
bob.sp.extrapolate_nearest(a, a_ext)
-
+
if gpu == True:
import xbob.cusp
return xbob.cusp.conv(a_ext, b)
else:
return scipy.signal.convolve2d(a_ext, b, 'valid')
#return = self.convfft(a_ext, b)
-
-
-
--
GitLab