diff --git a/README.rst b/README.rst
index 97f135e6f8840646f49985c9ae12862ef4df0806..648ca3a2fb235d041a75ffdc5cbbf5bff58b98dc 100644
--- a/README.rst
+++ b/README.rst
@@ -47,7 +47,8 @@ To achieve this goal, interfaces to some publicly available vein image
databases are contained, and default evaluation protocols are defined, e.g.:
- UTFVP - University of Twente Finger Vein Database [http://www.sas.ewi.utwente.nl/]
-- VERA Finger vein Database [http://www.idiap.ch/scientific-research/resources]
+- VERA - Finger vein Database [http://www.idiap.ch/scientific-research/resources]
+- PUT - The PUT biometric vein (palm and wrist) recognition dataset [http://biometrics.put.poznan.pl/vein-dataset/]
Algorithms
@@ -97,7 +98,8 @@ this package using the same installation mechanism.
Development
-----------
-In order to develop the latest version of this package::
+In order to develop the latest version of this package, install Bob_ as
+indicated above. Once that is done, do this::
$ git clone https://gitlab.idiap.ch/biometrc/bob.bio.vein.git
$ cd bob.bio.vein
diff --git a/bob/bio/vein/algorithms/MiuraMatch.py b/bob/bio/vein/algorithms/MiuraMatch.py
index 499d157e7720c65f125b025aeff5161b7233b91d..17068bb7a20b75b29c1c45c8582f86bd78cfc99c 100644
--- a/bob/bio/vein/algorithms/MiuraMatch.py
+++ b/bob/bio/vein/algorithms/MiuraMatch.py
@@ -47,7 +47,6 @@ class MiuraMatch (Algorithm):
"""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)
diff --git a/bob/bio/vein/extractors/LocalBinaryPatterns.py b/bob/bio/vein/extractors/LocalBinaryPatterns.py
index 221580684df540f301d99ff3ef800a6a0bff4cab..c799c56105e49125d3da27aea06a8d866d219bd3 100644
--- a/bob/bio/vein/extractors/LocalBinaryPatterns.py
+++ b/bob/bio/vein/extractors/LocalBinaryPatterns.py
@@ -80,8 +80,6 @@ class LocalBinaryPatterns (Extractor):
"""Copies the given array into the given blocks"""
# fill array in the desired shape
- #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][:]
@@ -90,9 +88,6 @@ class LocalBinaryPatterns (Extractor):
"""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)
@@ -113,9 +108,6 @@ class LocalBinaryPatterns (Extractor):
# create new array
lbphs_array = numpy.zeros(shape, 'float64')
- #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)
diff --git a/bob/bio/vein/extractors/RepeatedLineTracking.py b/bob/bio/vein/extractors/RepeatedLineTracking.py
index 4321c9562cf630812a1c84002cf496f03f52fc08..9f87d92a23b20486c6eeff107eb31e6fbaa37284 100644
--- a/bob/bio/vein/extractors/RepeatedLineTracking.py
+++ b/bob/bio/vein/extractors/RepeatedLineTracking.py
@@ -1,8 +1,9 @@
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
-import numpy
import math
+import numpy
+import scipy.ndimage
import bob.core
import bob.io.base
@@ -198,14 +199,6 @@ class RepeatedLineTracking (Extractor):
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()
- #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)
diff --git a/bob/bio/vein/preprocessors/FingerCrop.py b/bob/bio/vein/preprocessors/FingerCrop.py
index 218f38aa97e26cbe6dd3c3a91271d2a95c537f3f..4492af76e7f1e4de0b33cbdf9acdaf20b6a11c31 100644
--- a/bob/bio/vein/preprocessors/FingerCrop.py
+++ b/bob/bio/vein/preprocessors/FingerCrop.py
@@ -329,7 +329,6 @@ class FingerCrop (Preprocessor):
def __HE__(self, image):
#Umbralization based on the pixels non zero
- #import ipdb; ipdb.set_trace()
imageEnhance = numpy.zeros(image.shape)
imageEnhance = imageEnhance.astype(numpy.uint8)
@@ -372,7 +371,6 @@ class FingerCrop (Preprocessor):
imageEnhance = utils.imfilter(image, gaborfilter, self.gpu, conv=False)
imageEnhance = numpy.abs(imageEnhance)
- #import ipdb; ipdb.set_trace()
imageEnhance = bob.core.convert(imageEnhance,numpy.uint8,(0,255),(imageEnhance.min(),imageEnhance.max()))
return imageEnhance
@@ -388,8 +386,6 @@ class FingerCrop (Preprocessor):
b = 1.2
n = 2.0
- #import ipdb; ipdb.set_trace()
-
#Convert image to doubles
image_new = bob.core.convert(image,numpy.float64,(0,1),(0,255))
img_h, img_w = image_new.shape
@@ -459,7 +455,6 @@ class FingerCrop (Preprocessor):
def crop_finger(self, image):
spoofingValue = self.__spoofingdetector__(image)
- #import ipdb; ipdb.set_trace()
#Padding array
image = self.__padding_finger__(image)
diff --git a/bob/bio/vein/tests/matching/0001_2_1_120509-135338.mat b/bob/bio/vein/tests/algorithms/0001_2_1_120509-135338.mat
similarity index 100%
rename from bob/bio/vein/tests/matching/0001_2_1_120509-135338.mat
rename to bob/bio/vein/tests/algorithms/0001_2_1_120509-135338.mat
diff --git a/bob/bio/vein/tests/matching/0001_2_2_120509-135558.mat b/bob/bio/vein/tests/algorithms/0001_2_2_120509-135558.mat
similarity index 100%
rename from bob/bio/vein/tests/matching/0001_2_2_120509-135558.mat
rename to bob/bio/vein/tests/algorithms/0001_2_2_120509-135558.mat
diff --git a/bob/bio/vein/tests/matching/0003_2_1_120509-141255.mat b/bob/bio/vein/tests/algorithms/0003_2_1_120509-141255.mat
similarity index 100%
rename from bob/bio/vein/tests/matching/0003_2_1_120509-141255.mat
rename to bob/bio/vein/tests/algorithms/0003_2_1_120509-141255.mat
diff --git a/bob/bio/vein/tests/matching/001_L_1.mat b/bob/bio/vein/tests/algorithms/001_L_1.mat
similarity index 100%
rename from bob/bio/vein/tests/matching/001_L_1.mat
rename to bob/bio/vein/tests/algorithms/001_L_1.mat
diff --git a/bob/bio/vein/tests/matching/001_L_1_h4w40.mat b/bob/bio/vein/tests/algorithms/001_L_1_h4w40.mat
similarity index 100%
rename from bob/bio/vein/tests/matching/001_L_1_h4w40.mat
rename to bob/bio/vein/tests/algorithms/001_L_1_h4w40.mat
diff --git a/bob/bio/vein/tests/features/huangwl_input_fvr.mat b/bob/bio/vein/tests/extractors/huangwl_input_fvr.mat
similarity index 100%
rename from bob/bio/vein/tests/features/huangwl_input_fvr.mat
rename to bob/bio/vein/tests/extractors/huangwl_input_fvr.mat
diff --git a/bob/bio/vein/tests/features/huangwl_input_img.mat b/bob/bio/vein/tests/extractors/huangwl_input_img.mat
similarity index 100%
rename from bob/bio/vein/tests/features/huangwl_input_img.mat
rename to bob/bio/vein/tests/extractors/huangwl_input_img.mat
diff --git a/bob/bio/vein/tests/features/huangwl_output.mat b/bob/bio/vein/tests/extractors/huangwl_output.mat
similarity index 100%
rename from bob/bio/vein/tests/features/huangwl_output.mat
rename to bob/bio/vein/tests/extractors/huangwl_output.mat
diff --git a/bob/bio/vein/tests/features/miuramax_input_fvr.mat b/bob/bio/vein/tests/extractors/miuramax_input_fvr.mat
similarity index 100%
rename from bob/bio/vein/tests/features/miuramax_input_fvr.mat
rename to bob/bio/vein/tests/extractors/miuramax_input_fvr.mat
diff --git a/bob/bio/vein/tests/features/miuramax_input_img.mat b/bob/bio/vein/tests/extractors/miuramax_input_img.mat
similarity index 100%
rename from bob/bio/vein/tests/features/miuramax_input_img.mat
rename to bob/bio/vein/tests/extractors/miuramax_input_img.mat
diff --git a/bob/bio/vein/tests/features/miuramax_output.mat b/bob/bio/vein/tests/extractors/miuramax_output.mat
similarity index 100%
rename from bob/bio/vein/tests/features/miuramax_output.mat
rename to bob/bio/vein/tests/extractors/miuramax_output.mat
diff --git a/bob/bio/vein/tests/features/miurarlt_input_fvr.mat b/bob/bio/vein/tests/extractors/miurarlt_input_fvr.mat
similarity index 100%
rename from bob/bio/vein/tests/features/miurarlt_input_fvr.mat
rename to bob/bio/vein/tests/extractors/miurarlt_input_fvr.mat
diff --git a/bob/bio/vein/tests/features/miurarlt_input_img.mat b/bob/bio/vein/tests/extractors/miurarlt_input_img.mat
similarity index 100%
rename from bob/bio/vein/tests/features/miurarlt_input_img.mat
rename to bob/bio/vein/tests/extractors/miurarlt_input_img.mat
diff --git a/bob/bio/vein/tests/features/miurarlt_output.mat b/bob/bio/vein/tests/extractors/miurarlt_output.mat
similarity index 100%
rename from bob/bio/vein/tests/features/miurarlt_output.mat
rename to bob/bio/vein/tests/extractors/miurarlt_output.mat
diff --git a/bob/bio/vein/tests/preprocessing/0019_3_1_120509-160517.png b/bob/bio/vein/tests/preprocessors/0019_3_1_120509-160517.png
similarity index 100%
rename from bob/bio/vein/tests/preprocessing/0019_3_1_120509-160517.png
rename to bob/bio/vein/tests/preprocessors/0019_3_1_120509-160517.png
diff --git a/bob/bio/vein/tests/preprocessing/0019_3_1_120509-160517_fvr_lee_huang.mat b/bob/bio/vein/tests/preprocessors/0019_3_1_120509-160517_fvr_lee_huang.mat
similarity index 100%
rename from bob/bio/vein/tests/preprocessing/0019_3_1_120509-160517_fvr_lee_huang.mat
rename to bob/bio/vein/tests/preprocessors/0019_3_1_120509-160517_fvr_lee_huang.mat
diff --git a/bob/bio/vein/tests/preprocessing/0019_3_1_120509-160517_img_lee.mat b/bob/bio/vein/tests/preprocessors/0019_3_1_120509-160517_img_lee.mat
similarity index 100%
rename from bob/bio/vein/tests/preprocessing/0019_3_1_120509-160517_img_lee.mat
rename to bob/bio/vein/tests/preprocessors/0019_3_1_120509-160517_img_lee.mat
diff --git a/bob/bio/vein/tests/preprocessing/0019_3_1_120509-160517_img_lee_huang.mat b/bob/bio/vein/tests/preprocessors/0019_3_1_120509-160517_img_lee_huang.mat
similarity index 100%
rename from bob/bio/vein/tests/preprocessing/0019_3_1_120509-160517_img_lee_huang.mat
rename to bob/bio/vein/tests/preprocessors/0019_3_1_120509-160517_img_lee_huang.mat
diff --git a/bob/bio/vein/tests/test.py b/bob/bio/vein/tests/test.py
index 9622ee8e08d6349a39ad59ef23fd344e26fb4de3..20a5eaf632e1b885028c264d68b68b626401d646 100644
--- a/bob/bio/vein/tests/test.py
+++ b/bob/bio/vein/tests/test.py
@@ -13,6 +13,7 @@ import pkg_resources
import bob.io.base
import bob.io.matlab
+import bob.io.image
def F(parts):
@@ -23,17 +24,17 @@ def F(parts):
def test_finger_crop():
- #Test finger vein image preprocessing
+ #Test finger vein image preprocessors
- input_filename = F(('preprocessing', '0019_3_1_120509-160517.png'))
- output_img_filename = F(('preprocessing',
+ input_filename = F(('preprocessors', '0019_3_1_120509-160517.png'))
+ output_img_filename = F(('preprocessors',
'0019_3_1_120509-160517_img_lee_huang.mat'))
- output_fvr_filename = F(('preprocessing',
+ output_fvr_filename = F(('preprocessors',
'0019_3_1_120509-160517_fvr_lee_huang.mat'))
img = bob.io.base.load(input_filename)
- from bob.fingervein.preprocessing.FingerCrop import FingerCrop
+ from bob.bio.vein.preprocessors.FingerCrop import FingerCrop
FC = FingerCrop(4, 40, False, False)
#FC = FingerCrop(4, 40, False, 5, 0.2, False)
@@ -52,16 +53,16 @@ def test_miuramax():
#Maximum Curvature method against Matlab reference
- input_img_filename = F(('features', 'miuramax_input_img.mat'))
- input_fvr_filename = F(('features', 'miuramax_input_fvr.mat'))
- output_filename = F(('features', 'miuramax_output.mat'))
+ input_img_filename = F(('extractors', 'miuramax_input_img.mat'))
+ input_fvr_filename = F(('extractors', 'miuramax_input_fvr.mat'))
+ output_filename = F(('extractors', 'miuramax_output.mat'))
# Load inputs
input_img = bob.io.base.load(input_img_filename)
input_fvr = bob.io.base.load(input_fvr_filename)
# Apply Python implementation
- from bob.fingervein.features.MaximumCurvature import MaximumCurvature
+ from bob.bio.vein.extractors.MaximumCurvature import MaximumCurvature
MC = MaximumCurvature(5, False)
output_img = MC((input_img, input_fvr))
@@ -77,16 +78,16 @@ def test_miurarlt():
#Repeated Line Tracking method against Matlab reference
- input_img_filename = F(('features', 'miurarlt_input_img.mat'))
- input_fvr_filename = F(('features', 'miurarlt_input_fvr.mat'))
- output_filename = F(('features', 'miurarlt_output.mat'))
+ input_img_filename = F(('extractors', 'miurarlt_input_img.mat'))
+ input_fvr_filename = F(('extractors', 'miurarlt_input_fvr.mat'))
+ output_filename = F(('extractors', 'miurarlt_output.mat'))
# Load inputs
input_img = bob.io.base.load(input_img_filename)
input_fvr = bob.io.base.load(input_fvr_filename)
# Apply Python implementation
- from bob.fingervein.features.RepeatedLineTracking import RepeatedLineTracking
+ from bob.bio.vein.extractors.RepeatedLineTracking import RepeatedLineTracking
RLT = RepeatedLineTracking(3000, 1, 21, False)
output_img = RLT((input_img, input_fvr))
@@ -102,16 +103,16 @@ def test_huangwl():
#Wide Line Detector method against Matlab reference
- input_img_filename = F(('features', 'huangwl_input_img.mat'))
- input_fvr_filename = F(('features', 'huangwl_input_fvr.mat'))
- output_filename = F(('features', 'huangwl_output.mat'))
+ input_img_filename = F(('extractors', 'huangwl_input_img.mat'))
+ input_fvr_filename = F(('extractors', 'huangwl_input_fvr.mat'))
+ output_filename = F(('extractors', 'huangwl_output.mat'))
# Load inputs
input_img = bob.io.base.load(input_img_filename)
input_fvr = bob.io.base.load(input_fvr_filename)
# Apply Python implementation
- from bob.fingervein.features.WideLineDetector import WideLineDetector
+ from bob.bio.vein.extractors.WideLineDetector import WideLineDetector
WL = WideLineDetector(5, 1, 41, False)
output_img = WL((input_img, input_fvr))
@@ -123,17 +124,18 @@ def test_huangwl():
def test_miura_match():
- """Test matching: Match Ratio method against Matlab reference"""
- template_filename = F(('matching', '0001_2_1_120509-135338.mat'))
- probe_gen_filename = F(('matching', '0001_2_2_120509-135558.mat'))
- probe_imp_filename = F(('matching', '0003_2_1_120509-141255.mat'))
+ #Match Ratio method against Matlab reference
+
+ template_filename = F(('algorithms', '0001_2_1_120509-135338.mat'))
+ probe_gen_filename = F(('algorithms', '0001_2_2_120509-135558.mat'))
+ probe_imp_filename = F(('algorithms', '0003_2_1_120509-141255.mat'))
template_vein = bob.io.base.load(template_filename)
probe_gen_vein = bob.io.base.load(probe_gen_filename)
probe_imp_vein = bob.io.base.load(probe_imp_filename)
- from bob.fingervein.tools.MiuraMatch import MiuraMatch
+ from bob.bio.vein.algorithms.MiuraMatch import MiuraMatch
MM = MiuraMatch(ch=18, cw=28)
score_gen = MM.score(template_vein, probe_gen_vein)
diff --git a/buildout.cfg b/buildout.cfg
index fe03cdfbb5e1b90aa440637d86fa1e7c11c7cd4a..303cff70d1dd40d1f696142cafa2b228ebcd8bf0 100644
--- a/buildout.cfg
+++ b/buildout.cfg
@@ -10,12 +10,14 @@ eggs = bob.bio.vein
bob.extension
gridtk
develop = src/bob.db.vera
+ src/bob.db.putvein
.
auto-checkout = *
newest = false
[sources]
bob.db.vera = git git@github.com:bioidiap/bob.db.vera
+bob.db.putvein = git git@gitlab.idiap.ch:biometric/bob.db.putvein
[scripts]
recipe = bob.buildout:scripts
diff --git a/doc/references.rst b/doc/references.rst
index 07af04f28260307e15b286ab7d5cc7aeaa7bc8f4..962d16145840fa924ccd5975300c9630e29a31b9 100644
--- a/doc/references.rst
+++ b/doc/references.rst
@@ -5,18 +5,18 @@
References
==========
-.. [LLP09]_ *E.C. Lee, H.C. Lee and K.R. Park. Finger vein recognition using minutia-based alignment and local binary pattern-based feature extraction. International Journal of Imaging Systems and Technology. Vol. 19, No. 3, pp. 175-178, September 2009.
+.. [KUU02]_ M. Kono, H. Ueki and S. Umemura. Near-infrared finger vein patterns for personal identification, Applied Optics, Vol. 41, Issue 35, pp. 7429-7436 (2002).
-.. [KUU02]_ *M. Kono, H. Ueki and S. Umemura. Near-infrared finger vein patterns for personal identification, Applied Optics, Vol. 41, Issue 35, pp. 7429-7436 (2002).
+.. [MNM04]_ 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.
-.. [ZY09]_ *J. Zhang and J. Yang. Finger-vein image enhancement based on combination of gray-level grouping and circular gabor filter. In International Conference on Information Engineering and Computer Science (ICIECS), pp. 1-4, Dec 2009.
+.. [MNM05]_ 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, Vol. 9 , pp. 347--350, 2005.
-.. [ZTXL09]_ *Jianjun Zhao, Hogliang Tian, Weixing Xu, and Xin Li. A New Approach to Hand Vein Image Enhancement. In IEEE Second International Conference on Intelligent Computation Technology and Automation, ICICTA. Vol. 1, pp. 499-501. 2009.
+.. [LLP09]_ E.C. Lee, H.C. Lee and K.R. Park. Finger vein recognition using minutia-based alignment and local binary pattern-based feature extraction. International Journal of Imaging Systems and Technology. Vol. 19, No. 3, pp. 175-178, September 2009.
-.. [MNM05]_ *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, Vol. 9 , pp. 347--350, 2005.
+.. [ZY09]_ J. Zhang and J. Yang. Finger-vein image enhancement based on combination of gray-level grouping and circular gabor filter. In International Conference on Information Engineering and Computer Science (ICIECS), pp. 1-4, Dec 2009.
-.. [MNM04]_ *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.
+.. [ZTXL09]_ Jianjun Zhao, Hogliang Tian, Weixing Xu, and Xin Li. A New Approach to Hand Vein Image Enhancement. In IEEE Second International Conference on Intelligent Computation Technology and Automation, ICICTA. Vol. 1, pp. 499-501. 2009.
-.. [HDLTL10]_ *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.
+.. [HDLTL10]_ 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.
-.. [MD13]_ *L. Mirmohamadsadeghi and A. Drygajlo. Palm vein recognition uisng local texture patterns, IET Biometrics, pp. 1-9, 2013.
+.. [MD13]_ L. Mirmohamadsadeghi and A. Drygajlo. Palm vein recognition using local texture patterns, IET Biometrics, pp. 1-9, 2013.
diff --git a/requirements.txt b/requirements.txt
index c78467094456c0de2c4c214d6e6d6c19580473ec..35ecb8e09fc323229ccc0fedc5d72430c83a5d63 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,8 +6,10 @@ argparse
bob.extension
bob.core
bob.io.base
-bob.ip.base
bob.io.matlab
+bob.io.image
+bob.ip.base
bob.bio.base
bob.db.utfvp
bob.db.vera
+bob.db.putvein