Source code for bob.bio.vein.extractor.PrincipalCurvature

#!/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)