#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
import numpy
from scipy.ndimage import gaussian_filter
from bob.bio.base.extractor import Extractor
[docs]class PrincipalCurvature(Extractor):
"""MiuraMax feature extractor
Based on [CW09]_.
"""
def __init__(
self,
sigma=3, # Gaussian standard deviation applied
threshold=4, # 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,
)
# block parameters
self.sigma = sigma
self.threshold = threshold
[docs] def ut_gauss(self, img, sigma, dx, dy):
return gaussian_filter(numpy.float64(img), sigma, order=[dx, dy])
[docs] 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 # noqa: E712
sigma = numpy.sqrt(self.sigma**2 / 2)
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
# Apply threshold
gamma = (self.threshold / 100) * numpy.max(Gmag)
indices = numpy.where(Gmag < gamma)
gx[indices] = 0
gy[indices] = 0
# Normalize
Gmag[numpy.where(Gmag == 0)] = 1 # Avoid dividing by zero
gx = gx / Gmag
gy = gy / Gmag
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 - numpy.min(veins[:])
veins = veins / numpy.max(veins[:])
veins = veins * finger_mask
# Binarise the vein image by otsu
md = numpy.median(veins[veins > 0])
img_veins_bin = 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"""
finger_image = image[
0
] # Normalized image with or without histogram equalization
finger_mask = image[1]
return self.principal_curvature(finger_image, finger_mask)