Commit f0ccd1b7 authored by Hatef OTROSHI's avatar Hatef OTROSHI

debug and adding Principal Curvature (PC) algorithm

parent c1d98486
Pipeline #44786 failed with stage
in 15 minutes
#!/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]_.
"""
......@@ -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
......@@ -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
......@@ -40,6 +40,7 @@ setup(
# baselines
'mc = bob.bio.vein.configurations.maximum_curvature',
'pc = bob.bio.vein.configurations.principal_curvature',
'rlt = bob.bio.vein.configurations.repeated_line_tracking',
'wld = bob.bio.vein.configurations.wide_line_detector',
......
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