diff --git a/bob/bio/vein/configurations/principal_curvature.py b/bob/bio/vein/configurations/principal_curvature.py
new file mode 100644
index 0000000000000000000000000000000000000000..8357ea2a5a92e104a63f7df2312abe7413704377
--- /dev/null
+++ b/bob/bio/vein/configurations/principal_curvature.py
@@ -0,0 +1,50 @@
+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# Tue 03 Nov 2020 16:48:32 CEST
+
+'''Principal Curvature and Miura Matching baseline
+
+References:
+
+1. [CW09]_
+2. [TV13]_
+3. [TVM14]_
+
+'''
+
+sub_directory = 'pc'
+"""Sub-directory where results will be placed.
+
+You may change this setting using the ``--sub-directory`` command-line option
+or the attribute ``sub_directory`` in a configuration file loaded **after**
+this resource.
+"""
+
+from ..preprocessor import NoCrop, TomesLeeMask, HuangNormalization, \
+ NoFilter, Preprocessor
+
+preprocessor = Preprocessor(
+ crop=NoCrop(),
+ mask=TomesLeeMask(),
+ normalize=HuangNormalization(),
+ filter=NoFilter(),
+ )
+"""Preprocessing using gray-level based finger cropping and no post-processing
+"""
+
+from ..extractor import PrincipalCurvature
+extractor = PrincipalCurvature()
+"""Features are the output of the principal curvature algorithm, as described on
+[CW09]_.
+
+Defaults taken from [CW09]_.
+"""
+
+# Notice the values of ch and cw are different than those from the
+# repeated-line tracking baseline.
+from ..algorithm import MiuraMatch
+algorithm = MiuraMatch()
+"""Miura-matching algorithm with specific settings for search displacement
+
+Defaults taken from [TV13]_.
+"""
diff --git a/bob/bio/vein/extractor/PrincipalCurvature.py b/bob/bio/vein/extractor/PrincipalCurvature.py
index c08cae315ea7e2c571cfe17565372f50962cc24c..3a8f97f324d7b51e655b0fcf7799ef1a77604385 100644
--- a/bob/bio/vein/extractor/PrincipalCurvature.py
+++ b/bob/bio/vein/extractor/PrincipalCurvature.py
@@ -7,87 +7,97 @@ import bob.io.base
from bob.bio.base.extractor import Extractor
+from scipy.ndimage import gaussian_filter
+
class PrincipalCurvature (Extractor):
- """MiuraMax feature 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)
- """
+ 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
- ):
+ def __init__(
+ self,
+ sigma = 3, # Gaussian standard deviation applied
+ threshold = 2, # Percentage of maximum used for hard thresholding
+ ):
+ """NOTE: In the reference paper where the size of the finger image is 320 by 128,
+ the proposed values for sigma and threshold are 3 and 4, respectively.
+ However, for other resolutions it is better to change the values for sigma and
+ threshold. e.g., in UTFVP dataset where the size of the finger image is 672 by 380,
+ sigma=6 and threshold=4 workes better results better features.
+ """
+ # call base class constructor
+ Extractor.__init__(
+ self,
+ sigma = sigma,
+ threshold = threshold,
+ )
- # call base class constructor
- Extractor.__init__(
- self,
- sigma = sigma,
- threshold = threshold,
- )
+ # block parameters
+ self.sigma = sigma
+ self.threshold = threshold
- # block parameters
- self.sigma = sigma
- self.threshold = threshold
+ def ut_gauss(self, img, sigma, dx, dy):
+ return gaussian_filter(numpy.float64(img), sigma, order = [dx,dy])
- 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 = numpy.zeros(mask.shape)
+ finger_mask[mask == True] = 1
- sigma = numpy.sqrt(self.sigma**2/2)
+ sigma = numpy.sqrt(self.sigma**2/2)
- gx = ut_gauss(img,sigma,1,0)
- gy = ut_gauss(img,sigma,0,1)
+ gx = self.ut_gauss(image,self.sigma,1,0)
+ gy = self.ut_gauss(image,self.sigma,0,1)
- Gmag = numpy.sqrt(gx**2 + gy**2) # Gradient magnitude
+ Gmag = numpy.sqrt(gx**2 + gy**2) # Gradient magnitude
- # Apply threshold
- gamma = (self.threshold/100)*max(max(Gmag))
+ # Apply threshold
+ gamma = (self.threshold/100)*numpy.max(Gmag)
- indices = find(Gmag < gamma)
+ indices = numpy.where(Gmag < gamma)
- gx[indices] = 0
- gy[indices] = 0
+ gx[indices] = 0
+ gy[indices] = 0
- # Normalize
- Gmag[find[Gmag==0]] = 1 # Avoid dividing by zero
- gx = gx/Gmag
- gy = gy/Gmag
+ # Normalize
+ Gmag[numpy.where(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)
+ hxx = self.ut_gauss(gx,sigma,1,0)
+ hxy = self.ut_gauss(gx,sigma,0,1)
+ hyy = self.ut_gauss(gy,sigma,0,1)
- lambda1 = 0.5*(hxx + hyy + numpy.sqrt(hxx**2 + hyy**2 - 2*hxx**hyy + 4*hxy**2))
- veins = lambda1*finger_mask
- # Normalise
- veins = veins - min(veins[:])
- veins = veins/max(veins[:])
+ lambda1 = 0.5*(hxx + hyy + numpy.sqrt(hxx**2 + hyy**2 - 2*hxx*hyy + 4*hxy**2))
+ veins = lambda1*finger_mask
- veins = veins*finger_mask
+ # Normalise
+ veins = veins - numpy.min(veins[:])
+ veins = veins/numpy.max(veins[:])
+ veins = veins*finger_mask
- # Binarise the vein image by otsu
- md = numpy.median(img_veins[img_veins>0])
- img_veins_bin = img_veins > md
+ # Binarise the vein image by otsu
+ md = numpy.median(veins[veins>0])
+ img_veins_bin = veins > md
- return img_veins_bin.astype(numpy.float64)
+ 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]
+ finger_image = image[0] #Normalized image with or without histogram equalization
+ finger_mask = image[1]
- return self.principal_curvature(finger_image, finger_mask)
+ return self.principal_curvature(finger_image, finger_mask)
\ No newline at end of file
diff --git a/doc/references.rst b/doc/references.rst
index 5265c8692e7e35d4edf62989ca46c4ca551abed2..8e5e16c8d0c672374f0ca175c0b9f368e1cfde34 100644
--- a/doc/references.rst
+++ b/doc/references.rst
@@ -27,3 +27,4 @@
.. [KK10] *R. Kabacinski and *M. Kowalski, **Human Vein Pattern Segmentation from Low Quality Images - A Comparison of Methods**, in: Image Processing and Communications Challenges 2, pp. 105-112, 2010.
+.. [CW09] 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.
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 7e146a7af885a87905ad99559844287082a051e0..9c8c358a729effe54757b85f2b94f277105c4d5d 100644
--- a/setup.py
+++ b/setup.py
@@ -2,106 +2,91 @@
# vim: set fileencoding=utf-8 :
from setuptools import setup, dist
-dist.Distribution(dict(setup_requires=['bob.extension']))
+
+dist.Distribution(dict(setup_requires=["bob.extension"]))
from bob.extension.utils import load_requirements, find_packages
+
install_requires = load_requirements()
setup(
-
- name='bob.bio.vein',
+ name="bob.bio.vein",
version=open("version.txt").read().rstrip(),
- description='Vein Recognition Library',
-
- url='https://gitlab.idiap.ch/bob/bob.bio.vein',
- license='GPLv3',
-
- author='Andre Anjos,Pedro Tome',
- author_email='andre.anjos@idiap.ch,pedro.tome@idiap.ch',
-
- keywords = "bob, biometric recognition, evaluation, vein",
-
- long_description=open('README.rst').read(),
-
+ description="Vein Recognition Library",
+ url="https://gitlab.idiap.ch/bob/bob.bio.vein",
+ license="GPLv3",
+ author="Andre Anjos,Pedro Tome",
+ author_email="andre.anjos@idiap.ch,pedro.tome@idiap.ch",
+ keywords="bob, biometric recognition, evaluation, vein",
+ long_description=open("README.rst").read(),
packages=find_packages(),
include_package_data=True,
- zip_safe = False,
-
+ zip_safe=False,
install_requires=install_requires,
-
entry_points={
-
- 'bob.bio.config': [
- # protocols (must be specified before the database in the cmd)
- # verafinger
- 'Nom = bob.bio.vein.config.database.protocol_verafinger.nom',
- 'Cropped-Nom = bob.bio.vein.config.database.protocol_verafinger.cropped_nom',
- 'Full = bob.bio.vein.config.database.protocol_verafinger.full',
- 'Cropped-Full = bob.bio.vein.config.database.protocol_verafinger.cropped_full',
- # utfvp
- 'nom = bob.bio.vein.config.database.protocol_utfvp.nom',
- 'full = bob.bio.vein.config.database.protocol_utfvp.full',
- '1vsall = bob.bio.vein.config.database.protocol_utfvp.one_vs_all',
- # fv3d
- 'central = bob.bio.vein.config.database.protocol_fv3d.central',
- 'left = bob.bio.vein.config.database.protocol_fv3d.left',
- 'right = bob.bio.vein.config.database.protocol_fv3d.right',
- 'stitched = bob.bio.vein.config.database.protocol_fv3d.stitched',
- # putvein
- 'wrist-LR-1 = bob.bio.vein.config.database.protocol_putvein.wrist_lr_1',
- 'wrist-LR-4 = bob.bio.vein.config.database.protocol_putvein.wrist_lr_4',
- 'palm-LR-1 = bob.bio.vein.config.database.protocol_putvein.palm_lr_1',
- 'palm-LR-4 = bob.bio.vein.config.database.protocol_putvein.palm_lr_4',
-
- # legacy databases
- 'verafinger = bob.bio.vein.config.database.verafinger',
- 'utfvp = bob.bio.vein.config.database.utfvp',
- 'fv3d = bob.bio.vein.config.database.fv3d',
- 'putvein = bob.bio.vein.config.database.putvein',
-
- # legacy baselines
- 'mc = bob.bio.vein.config.maximum_curvature',
- 'rlt = bob.bio.vein.config.repeated_line_tracking',
- 'wld = bob.bio.vein.config.wide_line_detector',
-
- # other (legacy)
- 'parallel = bob.bio.vein.config.parallel',
- 'gridio4g48 = bob.bio.vein.config.gridio4g48',
- 'grid = bob.bio.vein.config.gridio4g48',
+ "bob.bio.config": [
+ # protocols (must be specified before the database in the cmd)
+ # verafinger
+ "Nom = bob.bio.vein.config.database.protocol_verafinger.nom",
+ "Cropped-Nom = bob.bio.vein.config.database.protocol_verafinger.cropped_nom",
+ "Full = bob.bio.vein.config.database.protocol_verafinger.full",
+ "Cropped-Full = bob.bio.vein.config.database.protocol_verafinger.cropped_full",
+ # utfvp
+ "nom = bob.bio.vein.config.database.protocol_utfvp.nom",
+ "full = bob.bio.vein.config.database.protocol_utfvp.full",
+ "1vsall = bob.bio.vein.config.database.protocol_utfvp.one_vs_all",
+ # fv3d
+ "central = bob.bio.vein.config.database.protocol_fv3d.central",
+ "left = bob.bio.vein.config.database.protocol_fv3d.left",
+ "right = bob.bio.vein.config.database.protocol_fv3d.right",
+ "stitched = bob.bio.vein.config.database.protocol_fv3d.stitched",
+ # putvein
+ "wrist-LR-1 = bob.bio.vein.config.database.protocol_putvein.wrist_lr_1",
+ "wrist-LR-4 = bob.bio.vein.config.database.protocol_putvein.wrist_lr_4",
+ "palm-LR-1 = bob.bio.vein.config.database.protocol_putvein.palm_lr_1",
+ "palm-LR-4 = bob.bio.vein.config.database.protocol_putvein.palm_lr_4",
+ # legacy databases
+ "verafinger = bob.bio.vein.config.database.verafinger",
+ "utfvp = bob.bio.vein.config.database.utfvp",
+ "fv3d = bob.bio.vein.config.database.fv3d",
+ "putvein = bob.bio.vein.config.database.putvein",
+ # legacy baselines
+ "mc = bob.bio.vein.config.maximum_curvature",
+ "rlt = bob.bio.vein.config.repeated_line_tracking",
+ "wld = bob.bio.vein.config.wide_line_detector",
+ # other (legacy)
+ "parallel = bob.bio.vein.config.parallel",
+ "gridio4g48 = bob.bio.vein.config.gridio4g48",
+ "grid = bob.bio.vein.config.gridio4g48",
],
-
- 'bob.bio.database': [
- 'verafinger = bob.bio.vein.config.database.verafinger:database',
- 'utfvp = bob.bio.vein.config.database.utfvp:database',
- 'fv3d = bob.bio.vein.config.database.fv3d:database',
- 'putvein = bob.bio.vein.config.database.putvein:database',
- ],
-
- 'bob.bio.pipeline': [
- 'wld = bob.bio.vein.config.wide_line_detector:pipeline',
- 'mc = bob.bio.vein.config.maximum_curvature:pipeline',
- 'rlt = bob.bio.vein.config.repeated_line_tracking:pipeline',
- ],
-
- 'console_scripts': [
- 'bob_bio_vein_compare_rois.py = bob.bio.vein.script.compare_rois:main',
- 'bob_bio_vein_view_sample.py = bob.bio.vein.script.view_sample:main',
- 'bob_bio_vein_blame.py = bob.bio.vein.script.blame:main',
- 'bob_bio_vein_markdet.py = bob.bio.vein.script.markdet:main',
- 'bob_bio_vein_watershed_mask.py = bob.bio.vein.script.watershed_mask:main',
- ]
- },
-
- classifiers = [
- 'Framework :: Bob',
- 'Development Status :: 4 - Beta',
- 'Intended Audience :: Science/Research',
- 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
- 'Natural Language :: English',
- 'Programming Language :: Python',
- 'Programming Language :: Python :: 3',
- 'Topic :: Scientific/Engineering :: Artificial Intelligence',
- 'Topic :: Software Development :: Libraries :: Python Modules',
- ],
-
+ "bob.bio.database": [
+ "verafinger = bob.bio.vein.config.database.verafinger:database",
+ "utfvp = bob.bio.vein.config.database.utfvp:database",
+ "fv3d = bob.bio.vein.config.database.fv3d:database",
+ "putvein = bob.bio.vein.config.database.putvein:database",
+ ],
+ "bob.bio.pipeline": [
+ "wld = bob.bio.vein.config.wide_line_detector:pipeline",
+ "mc = bob.bio.vein.config.maximum_curvature:pipeline",
+ "rlt = bob.bio.vein.config.repeated_line_tracking:pipeline",
+ ],
+ "console_scripts": [
+ "bob_bio_vein_compare_rois.py = bob.bio.vein.script.compare_rois:main",
+ "bob_bio_vein_view_sample.py = bob.bio.vein.script.view_sample:main",
+ "bob_bio_vein_blame.py = bob.bio.vein.script.blame:main",
+ "bob_bio_vein_markdet.py = bob.bio.vein.script.markdet:main",
+ "bob_bio_vein_watershed_mask.py = bob.bio.vein.script.watershed_mask:main",
+ ],
+ },
+ classifiers=[
+ "Framework :: Bob",
+ "Development Status :: 4 - Beta",
+ "Intended Audience :: Science/Research",
+ "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
+ "Natural Language :: English",
+ "Programming Language :: Python",
+ "Programming Language :: Python :: 3",
+ "Topic :: Scientific/Engineering :: Artificial Intelligence",
+ "Topic :: Software Development :: Libraries :: Python Modules",
+ ],
)