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GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
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How to Apply These Terms to Your New Programs
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<one line to give the program's name and a brief idea of what it does.>
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Also add information on how to contact you by electronic and paper mail.
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This is free software, and you are welcome to redistribute it
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The hypothetical commands `show w' and `show c' should show the appropriate
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# SPDX-FileCopyrightText: © 2018 Idiap Research Institute <contact@idiap.ch>
# SPDX-FileContributor: Vedrana Krivokuca Hahn <vkrivokuca@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-only
include COPYING README.rst requirements.txt environment.yml buildout.cfg
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.. SPDX-FileCopyrightText: © 2018 Idiap Research Institute <contact@idiap.ch>
.. SPDX-FileContributor: Vedrana Krivokuca Hahn <vkrivokuca@idiap.ch>
..
.. SPDX-License-Identifier: GPL-3.0-only
==========================================================================================
Towards Quantifying the Entropy of Fingervein Patterns across Different Feature Extractors
==========================================================================================
This package is part of the signal-processing and machine learning toolbox
Bob_. It contains the source code to reproduce the following paper::
@inproceedings{Krivokuca_ISBA2018_2018,
title={Towards quantifying the entropy of fingervein patterns across different feature extractors},
author={Krivoku{\'{c}}a, Vedrana and Marcel, S{\'{e}}bastien},
booktitle={2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA)},
pages={1--8},
year={2018},
organization={IEEE}
}
If you use this package and/or its results, please cite both the package and the corresponding paper_. Also, please ensure that you include the two original Bob references (Bob_) in your citations.
Software Installation
---------------------
The first thing you must do is to install all the required software to reproduce the experiments in this paper. Note that the installation instructions are based on conda_ and that, for this paper, we currently only support Python 2.7 on Linux 64-bit architectures. Keeping this in mind, proceed with the installation as follows:
1. `Install Miniconda`_.
2. Download the paper source code and extract the files into your working directory.
3. Navigate to your working directory as follows:
.. code-block:: sh
$ cd [path_to_your_working_directory]
4. Create a conda environment containing all the necessary package dependencies, using the following command:
.. code-block:: sh
$ conda env create -f environment.yml
5. Activate the environment as follows:
.. code-block:: sh
$ source activate bob.paper.isba2018_entropy.env
6. Install the paper package to generate the necessary scripts in your working directory as follows:
.. code-block:: sh
$ buildout
Downloading the Databases
-------------------------
The next thing you must do is to download the two fingervein databases used in our experiments: VERA and UTFVP. Please refer to the following websites to do this:
* VERA_
* UTFVP_
Take note of the directories in which you have stored the downloaded databases. Then, create a textfile named ``.bob_bio_databases.txt`` and **store it in your home directory.** Inside this textfile, insert the following two lines:
.. code-block:: sh
[YOUR_VERAFINGER_DIRECTORY] = path_to_vera_database
[YOUR_UTFVP_DIRECTORY] = path_to_utfvp_database
Make sure you replace ``path_to_vera_database`` with the path to your downloaded VERA database and ``path_to_utfvp_database`` with the path to your downloaded UTFVP database.
Running the Experiments
-----------------------
You are now ready to run the experiments to reproduce the results in Table 2 and Figure 2 from our paper! The experiments are run in two parts:
1. Extract the fingervein features and compute the Hamming distance (HD) between every pair of feature vectors from different fingers, by executing the following commands (one at a time) in your terminal:
.. code-block:: sh
$ ./bin/verify.py vera-wld # WLD feature extractor on the VERA fingervein database
$ ./bin/verify.py vera-rlt # RLT feature extractor on the VERA fingervein database
$ ./bin/verify.py vera-mc # MC feature extractor on the VERA fingervein database
$ ./bin/verify.py utfvp-wld # WLD feature extractor on the UTFVP fingervein database
$ ./bin/verify.py utfvp-rlt # RLT feature extractor on the UTFVP fingervein database
$ ./bin/verify.py utfvp-mc # MC feature extractor on the UTFVP fingervein database
Please be aware that it might take a while for each of the above experiments to complete. This is particularly true for the RLT extractor and for the UTFVP database in general.
By default, all experimental results will be stored in the ``bob.paper.isba2018_entropy/results/`` directory, under the database-specific and extractor-specific sub-directories. For example, the results for the MC extractor on the VERA database will be stored in the ``bob.paper.isba2018_entropy/results/vera/mc/`` directory.
Inside each extractor's results directory, you will find the following sub-directories: ``preprocessed``, ``extracted``, ``models``, ``Full`` for VERA and ``full`` for UTFVP, and ``gridtk_logs``. These directories store the results of the full fingervein recognition pipeline, starting from preprocessing the images in the database to calculating the HDs between different fingervein feature vectors. In particular:
* ``preprocessed``: contains the preprocessed finger images;
* ``extracted``: contains the extracted fingervein features;
* ``models``: contains the enrolled fingervein features;
* ``Full/nonorm/scores-dev`` (VERA) or ``full/nonorm/scores-dev`` (UTFVP): textfile that contains the HD between every possible pair of extracted fingervein feature vectors (``Full`` or ``full`` is the name of the matching protocol used, which in this case just means that the HD is calculated between all possible pairs of finger samples);
* ``gridtk_logs``: contains messages logging the full experimental procedure.
While the above directories are useful for you to inspect the output of each stage of the experimental process, the only results you need to worry about for now will be produced in part 2 of the experimental procedure.
2. Calculate the entropy of the fingervein features from each extractor on each database, based on the previously-computed HDs (those calculated between the fingervein patterns from **different** fingers **only**), by executing the following commands (one at a time) in your terminal:
.. code-block:: sh
$ ./bin/calculate_entropy.py -d 'vera' -e 'wld' # WLD feature extractor on the VERA fingervein database
$ ./bin/calculate_entropy.py -d 'vera' -e 'rlt' # RLT feature extractor on the VERA fingervein database
$ ./bin/calculate_entropy.py -d 'vera' -e 'mc' # MC feature extractor on the VERA fingervein database
$ ./bin/calculate_entropy.py -d 'utfvp' -e 'wld' # WLD feature extractor on the UTFVP fingervein database
$ ./bin/calculate_entropy.py -d 'utfvp' -e 'rlt' # RLT feature extractor on the UTFVP fingervein database
$ ./bin/calculate_entropy.py -d 'utfvp' -e 'mc' # MC feature extractor on the UTFVP fingervein database
There are two outputs for each command in this step of the experiment. The first output is the set of statistics, including the mean, standard deviation, degrees of freedom, and entropy for the corresponding HD distribution, as per the results in Table 2 of the paper. These statistics are printed in the ``statistics.txt`` textfile of the results directory corresponding to each feature extractor and database. For example, for the MC extractor on the VERA database, these results are stored in ``bob.paper.isba2018_entropy/results/vera/mc/statistics.txt``.
The second output is the HD distribution plot with the corresponding binomial distribution overlaid, as per the results in Figure 2 of the paper. This plot is stored in the ``HD_distribution.pdf`` file, which is located in the same place as the corresponding ``statistics.txt`` textfile.
Note that, if you wish to work with just one extractor and one database at a time, you do not have to run all the commands from steps 1 and 2. For example, if you are only interested in the results for the RLT extractor on the UTFVP database, you would execute the following commands in your terminal (in the specified order):
.. code-block:: sh
$ ./bin/verify.py utfvp-rlt # Extract the RLT features and calculate the HDs on the UTFVP fingervein database
$ ./bin/calculate_entropy.py -d 'utfvp' -e 'rlt' # Calculate the entropy of the RLT features on the UTFVP fingervein database
Contact
-------
If you have any questions or issues relating to this software package, please contact Vedrana Krivokuca Hahn (vkrivokuca@idiap.ch or vedrana.krivokuca@idiap.ch) or our development `mailing list`_.
.. Links:
.. _Bob: https://www.idiap.ch/software/bob
.. _conda: https://conda.io
.. _Install Miniconda: https://conda.io/docs/user-guide/install/index.html
.. _paper: https://ieeexplore.ieee.org/document/8311473
.. _VERA: https://www.idiap.ch/dataset/vera-fingervein
.. _UTFVP: http://scs.ewi.utwente.nl/downloads/show,Finger%20Vein/
.. _mailing list: https://www.idiap.ch/software/bob/discuss
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# SPDX-FileCopyrightText: © 2018 Idiap Research Institute <contact@idiap.ch>
# SPDX-FileContributor: Vedrana Krivokuca Hahn <vkrivokuca@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-only
""" Configuration for calculating Hamming distances on fingervein features extracted using the Maximum Curvature feature extractor, for the UTFVP database """
# Database:
from bob.bio.vein.configurations.utfvp import database
protocol = 'full'
# Directory where results will be placed:
temp_directory = './results/utfvp' # pre-processed and extracted features will be placed here, along with the enrolled models
sub_directory = 'mc'
result_directory = temp_directory # Hamming distances will be placed here
from bob.io.base import create_directories_safe
create_directories_safe(temp_directory)
# Pre-processing based on locating the finger in the image and horizontally aligning it:
from bob.bio.vein.configurations.maximum_curvature import preprocessor
# Feature extraction based on the Maximum Curvature algorithm:
from bob.bio.vein.configurations.maximum_curvature import extractor
# Hamming distance calculation:
from bob.bio.vein.algorithm import HammingDistance
algorithm = HammingDistance()
# Set up parallel processing using all available processors:
from bob.bio.vein.configurations.parallel import parallel, nice
# Specify the level of detail to be output in the terminal during program execution:
verbose = 2
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# SPDX-FileCopyrightText: © 2018 Idiap Research Institute <contact@idiap.ch>
# SPDX-FileContributor: Vedrana Krivokuca Hahn <vkrivokuca@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-only
""" Configuration for calculating Hamming distances on fingervein features extracted using the Repeated Line Tracking feature extractor, for the UTFVP database """
# Database:
from bob.bio.vein.configurations.utfvp import database
protocol = 'full'
# Directory where results will be placed:
temp_directory = './results/utfvp' # pre-processed and extracted features will be placed here, along with the enrolled models
sub_directory = 'rlt'
result_directory = temp_directory # Hamming distances will be placed here
from bob.io.base import create_directories_safe
create_directories_safe(temp_directory)
# Pre-processing based on locating the finger in the image and horizontally aligning it:
from bob.bio.vein.configurations.repeated_line_tracking import preprocessor
# Feature extraction based on the Repeated Line Tracking algorithm:
from bob.bio.vein.configurations.repeated_line_tracking import extractor
# Hamming distance calculation:
from bob.bio.vein.algorithm import HammingDistance
algorithm = HammingDistance()
# Set up parallel processing using all available processors:
from bob.bio.vein.configurations.parallel import parallel, nice
# Specify the level of detail to be output in the terminal during program execution:
verbose = 2
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# SPDX-FileCopyrightText: © 2018 Idiap Research Institute <contact@idiap.ch>
# SPDX-FileContributor: Vedrana Krivokuca Hahn <vkrivokuca@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-only
""" Configuration for calculating Hamming distances on fingervein features extracted using the Wide Line Detector feature extractor, for the UTFVP database """
# Database:
from bob.bio.vein.configurations.utfvp import database
protocol = 'full'
# Directory where results will be placed:
temp_directory = './results/utfvp' # pre-processed and extracted features will be placed here, along with the enrolled models
sub_directory = 'wld'
result_directory = temp_directory # Hamming distances will be placed here
from bob.io.base import create_directories_safe
create_directories_safe(temp_directory)
# Pre-processing based on locating the finger in the image and horizontally aligning it:
from bob.bio.vein.configurations.wide_line_detector import preprocessor
# Feature extraction based on the Wide Line Detector algorithm:
from bob.bio.vein.configurations.wide_line_detector import extractor
# Hamming distance calculation:
from bob.bio.vein.algorithm import HammingDistance
algorithm = HammingDistance()
# Set up parallel processing using all available processors:
from bob.bio.vein.configurations.parallel import parallel, nice
# Specify the level of detail to be output in the terminal during program execution:
verbose = 2
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# SPDX-FileCopyrightText: © 2018 Idiap Research Institute <contact@idiap.ch>
# SPDX-FileContributor: Vedrana Krivokuca Hahn <vkrivokuca@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-only
""" Configuration for calculating Hamming distances on fingervein features extracted using the Maximum Curvature feature extractor, for the VERA database """
# Database:
from bob.bio.vein.configurations.verafinger import database
protocol = 'Full'
# Directory where results will be placed:
temp_directory = './results/vera' # pre-processed and extracted features will be placed here, along with the enrolled models
sub_directory = 'mc'
result_directory = temp_directory # Hamming distances will be placed here
from bob.io.base import create_directories_safe
create_directories_safe(temp_directory)
# Pre-processing based on locating the finger in the image and horizontally aligning it:
from bob.bio.vein.configurations.maximum_curvature import preprocessor
# Feature extraction based on the Maximum Curvature algorithm:
from bob.bio.vein.configurations.maximum_curvature import extractor
# Hamming distance calculation:
from bob.bio.vein.algorithm import HammingDistance
algorithm = HammingDistance()
# Set up parallel processing using all available processors:
from bob.bio.vein.configurations.parallel import parallel, nice
# Specify the level of detail to be output in the terminal during program execution:
verbose = 2
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# SPDX-FileCopyrightText: © 2018 Idiap Research Institute <contact@idiap.ch>
# SPDX-FileContributor: Vedrana Krivokuca Hahn <vkrivokuca@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-only
""" Configuration for calculating Hamming distances on fingervein features extracted using the Repeated Line Tracking feature extractor, for the VERA database """
# Database:
from bob.bio.vein.configurations.verafinger import database
protocol = 'Full'
# Directory where results will be placed:
temp_directory = './results/vera' # pre-processed and extracted features will be placed here, along with the enrolled models
sub_directory = 'rlt'
result_directory = temp_directory # Hamming distances will be placed here
from bob.io.base import create_directories_safe
create_directories_safe(temp_directory)
# Pre-processing based on locating the finger in the image and horizontally aligning it:
from bob.bio.vein.configurations.repeated_line_tracking import preprocessor
# Feature extraction based on the Repeated Line Tracking algorithm:
from bob.bio.vein.configurations.repeated_line_tracking import extractor
# Hamming distance calculation:
from bob.bio.vein.algorithm import HammingDistance
algorithm = HammingDistance()
# Set up parallel processing using all available processors:
from bob.bio.vein.configurations.parallel import parallel, nice
# Specify the level of detail to be output in the terminal during program execution:
verbose = 2
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# SPDX-FileCopyrightText: © 2018 Idiap Research Institute <contact@idiap.ch>
# SPDX-FileContributor: Vedrana Krivokuca Hahn <vkrivokuca@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-only
""" Configuration for calculating Hamming distances on fingervein features extracted using the Wide Line Detector feature extractor, for the VERA database """
# Database:
from bob.bio.vein.configurations.verafinger import database
protocol = 'Full'
# Directory where results will be placed:
temp_directory = './results/vera' # pre-processed and extracted features will be placed here, along with the enrolled models
sub_directory = 'wld'
result_directory = temp_directory # Hamming distances will be placed here
from bob.io.base import create_directories_safe
create_directories_safe(temp_directory)
# Pre-processing based on locating the finger in the image and horizontally aligning it:
from bob.bio.vein.configurations.wide_line_detector import preprocessor
# Feature extraction based on the Wide Line Detector algorithm:
from bob.bio.vein.configurations.wide_line_detector import extractor
# Hamming distance calculation:
from bob.bio.vein.algorithm import HammingDistance
algorithm = HammingDistance()
# Set up parallel processing using all available processors:
from bob.bio.vein.configurations.parallel import parallel, nice
# Specify the level of detail to be output in the terminal during program execution:
verbose = 2
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# SPDX-FileCopyrightText: © 2018 Idiap Research Institute <contact@idiap.ch>
# SPDX-FileContributor: Vedrana Krivokuca Hahn <vkrivokuca@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-only
""" This script calculates the entropy of fingervein patterns based on Hamming distance distributions between unrelated feature vectors """
import bob.measure
import numpy
import scipy
import scipy.stats
from matplotlib import pyplot as plt
plt.switch_backend('pdf') # switch to non-X backend
from matplotlib.backends.backend_pdf import PdfPages
import argparse
def calc_stats(HDs, db, extractor):
""" Calculates the mean, standard deviation, degrees of freedom, and entropy for the input Hamming Distances (HDs).
**Parameters:**
HDs (list): A list of Hamming distances between the fingervein patterns of every pair of different fingers
db (str): Name of the fingervein database for which we wish to calculate the statistics
extractor (str): The feature extractor used to extract the fingervein patterns on which the input HDs have been calculated
**Returns:**
p (float): The mean of the HD distribution and the fitted binomial distribution
sigma (float): The standard deviation of the HD distribution and the fitted binomial distribution
N (float): The number of degrees of freedom (approx. number of independent bits in the underlying fingervein feature vectors) for the fitted binomial distribution
H (float): The estimated entropy per independent bit for the underlying fingervein feature vectors
H_T (float): The estimated total entropy across all independent bits in the underlying fingervein feature vectors
statistics.txt (textfile): Contains the calculated statistics
"""
# Calculate the various statistics:
p = numpy.mean(HDs)
sigma = numpy.std(HDs)
N = (p * (1 - p)) / (sigma ** 2)
H = -p*scipy.log2(p) - (1 - p)*scipy.log2(1 - p)
H_T = H * N
# Write the statistics to a textfile:
filename = 'results/' + db + '/' + extractor + '/statistics.txt'
f = open(filename, "w")
f.write("Results from Table 2, for the %s extractor on the %s database:\n\n" % (extractor.upper(), db.upper()))
f.write("p = %.2f\n" % (p))
sigma_str = u'\N{GREEK SMALL LETTER SIGMA}'
f.write("%s = %.3f\n" % (sigma_str.encode('utf-8'), sigma))
f.write("N = %s\n" % (int(round(N))))
f.write("H = %.2f\n" % (H))
f.write("H_T = %s" % (int(round(H_T))))
f.close()
return p, sigma, N, H, H_T
def plot_distributions(HDs, db, extractor, p, sigma, N):
""" Plots the histogram of the input Hamming Distances (HDs) and fits a binomial to the distribution.
**Parameters:**
HDs (list): A list of Hamming distances between the fingervein patterns of every pair of different fingers
db (str): Name of the fingervein database for which we wish to calculate the statistics
extractor (str): The feature extractor used to extract the fingervein patterns on which the input HDs have been calculated
p (float): The mean of the HD distribution and the fitted binomial distribution
sigma (float): The standard deviation of the HD distribution and the fitted binomial distribution
N (float): The number of degrees of freedom (approx. number of independent bits in the underlying fingervein feature vectors) for the fitted binomial distribution
**Returns:**
HD_distribution.pdf (saved image): Plot of the HD distribution with the corresponding binomial distribution overlaid
"""
# Calculate the histogram of the Hamming distance (HD) distribution:
fig = plt.figure()
plt.xlim([0, 1])
plt.xticks([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])
plt.title('%s Extractor on %s Database' % (extractor.upper(), db.upper()))
plt.xlabel('HD')
plt.ylabel('Probability')
counts, bin_edges = numpy.histogram(HDs, bins=numpy.arange(0.000, 1.001, 0.001)) # bin the HD values
bin_probs = counts/float(len(HDs)) # normalise the bin counts so that every bin value gives the probability of that bin
bin_middles = (bin_edges[1:]+bin_edges[:-1])/float(2) # get the mid point of each bin
bin_width = bin_edges[1]-bin_edges[0] # compute the bin width
# Calculate the binomial distribution corresponding to the HD histogram, and plot the HD histogram overlaid with the binomial distribution:
N = int(round(N)) # degrees of freedom for the binomial distribution = degrees of freedom of HD distribution
x_temp = range(0, N+1)
x = [i/float(N) for i in x_temp] # fractional Hamming distances from 1/N to N/N (liken to probability of getting H = x * N heads from N coin tosses)
plt.plot(x, scipy.stats.binom.pmf(x_temp, N, p), '-k')
plt.ylim([0.0, 0.10])
plt.yticks([0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10])
# Normalize the histogram so it is easier to visualise the binomial distribution fit:
norm_scale = max(scipy.stats.binom.pmf(x_temp, N, p))/max(bin_probs)
normed_bin_probs = bin_probs * norm_scale
plt.bar(bin_middles, normed_bin_probs, width=bin_width, color='lightgrey')
# Save the figure:
fig_path = 'results/' + db + '/' + extractor + '/HD_distribution.pdf'
pdf = PdfPages(fig_path)
fig.savefig(fig_path, format='pdf', bbox_inches='tight')
plt.close()
def main():
""" Calculates the entropy of fingervein patterns.
This function calculates the mean, standard deviation, degrees of freedom, and entropy for the Hamming Distances (HDs) between fingervein feature vectors of different fingers.
It also plots the histogram of the HDs and fits a binomial to the distribution.
**Parameters (from the command line):**
database (str): Name of the fingervein database for which we wish to calculate the entropy
extractor (str): The feature extractor used to extract the fingervein patterns
**Returns:**
statistics.txt (textfile): Contains the calculated statistics
HD_distribution.pdf (saved image): Plot of the HD distribution with the corresponding binomial distribution overlaid
"""
# Parse the command line arguments:
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--database', required=True, choices = ('vera', 'utfvp'), help = "The fingervein database name.")
parser.add_argument('-e', '--extractor', required=True, choices = ('wld', 'rlt', 'mc'), help = "The fingervein feature extractor.")
args = parser.parse_args()
# Extract the previously-generated HDs for the specified database and feature extractor:
if args.database == 'vera':
scores_path = 'results/' + args.database + '/' + args.extractor + '/Full/nonorm/scores-dev'
elif args.database == 'utfvp':
scores_path = 'results/' + args.database + '/' + args.extractor + '/full/nonorm/scores-dev'
scores = bob.measure.load.split(scores_path)
HDs = scores[0]
print '================================================================='
print 'Database: %s, Extractor: %s' % (args.database.upper(), args.extractor.upper())
print 'Total number of scores used in entropy estimation = %s' % (len(HDs))
# Calculate the mean, standard deviation, degrees of freedom, and entropy for the HDs:
print 'Calculating statistics ...'
[p, sigma, N, H, H_T] = calc_stats(HDs, args.database, args.extractor)
# Plot the histogram of the HDs and fit a binomial to the distribution:
print 'Plotting distributions ...'
plot_distributions(HDs, args.database, args.extractor, p, sigma, N)
print '================================================================='
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# SPDX-FileCopyrightText: © 2018 Idiap Research Institute <contact@idiap.ch>
# SPDX-FileContributor: Vedrana Krivokuca Hahn <vkrivokuca@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-only
[buildout]
parts = scripts
develop = .
eggs = bob.paper.isba2018_entropy
extensions = bob.buildout
newest = false
verbose = true
[scripts]
recipe = bob.buildout:scripts
dependent-scripts = true
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# SPDX-FileCopyrightText: © 2018 Idiap Research Institute <contact@idiap.ch>
# SPDX-FileContributor: Vedrana Krivokuca Hahn <vkrivokuca@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-only
name: bob.paper.isba2018_entropy.env
channels:
- https://www.idiap.ch/software/bob/conda
- defaults
dependencies:
- alabaster=0.7.10=py27_0
- babel=2.5.0=py27_0
- backports=1.0=py27_0
- boost=1.61.0=py27_0
- cairo=1.14.8=0
- certifi=2016.2.28=py27_0
- coverage=4.4.1=py27_0
- cycler=0.10.0=py27_0
- dbus=1.10.20=0
- decorator=4.1.2=py27_0
- docopt=0.6.2=py27_0
- docutils=0.14=py27_0
- enum34=1.1.6=py27_0
- expat=2.1.0=0
- fontconfig=2.12.1=3
- freetype=2.5.5=2
- functools32=3.2.3.2=py27_0
- get_terminal_size=1.0.0=py27_0
- gflags=2.2.0=1
- glib=2.50.2=1
- glog=0.3.5=0
- gst-plugins-base=1.8.0=0
- gstreamer=1.8.0=0
- h5py=2.7.0=np112py27_0
- hdf5=1.8.17=2
- icu=54.1=0
- imagesize=0.7.1=py27_0
- ipython=5.3.0=py27_0
- ipython_genutils=0.2.0=py27_0
- jbig=2.1=0
- jinja2=2.9.6=py27_0
- jpeg=9b=0
- leveldb=1.20=0
- libffi=3.2.1=1
- libgcc=5.2.0=0
- libgfortran=3.0.0=1
- libiconv=1.14=0
- libpng=1.6.30=1
- libprotobuf=3.4.0=0
- libtiff=4.0.6=3
- libxcb=1.12=1
- libxml2=2.9.4=0
- lmdb=0.9.21=0
- markupsafe=1.0=py27_0
- matplotlib=2.0.2=np112py27_0
- mkl=2017.0.3=0
- networkx=1.11=py27_0
- nose=1.3.7=py27_1
- numpy=1.12.1=py27_0
- olefile=0.44=py27_0
- openblas=0.2.19=0
- openssl=1.0.2l=0
- pandas=0.20.3=py27_0
- path.py=10.3.1=py27_0
- pathlib2=2.3.0=py27_0
- pcre=8.39=1
- pexpect=4.2.1=py27_0
- pickleshare=0.7.4=py27_0
- pillow=4.2.1=py27_0
- pip=9.0.1=py27_1
- pixman=0.34.0=0
- prompt_toolkit=1.0.15=py27_0
- protobuf=3.4.0=py27_0
- ptyprocess=0.5.2=py27_0
- pycairo=1.10.0=py27_0
- pygments=2.2.0=py27_0
- pyparsing=2.2.0=py27_0
- pyqt=5.6.0=py27_2
- python=2.7.13=0
- python-dateutil=2.6.1=py27_0
- python-gflags=3.1.1=py27_0
- python-leveldb=0.194=py27_1
- pytz=2017.2=py27_0
- pywavelets=0.5.2=np112py27_0
- pyyaml=3.12=py27_0
- qt=5.6.2=5
- readline=6.2=2
- requests=2.14.2=py27_0
- scandir=1.5=py27_0
- scikit-image=0.13.0=np112py27_0
- scikit-learn=0.19.0=np112py27_0
- scipy=0.19.1=np112py27_0
- setuptools=36.4.0=py27_1
- simplegeneric=0.8.1=py27_1
- sip=4.18=py27_0
- six=1.10.0=py27_0
- snappy=1.1.6=0
- snowballstemmer=1.2.1=py27_0
- sphinx=1.6.3=py27_0
- sphinxcontrib=1.0=py27_0
- sphinxcontrib-websupport=1.0.1=py27_0
- sqlalchemy=1.1.13=py27_0
- sqlite=3.13.0=0
- subprocess32=3.2.7=py27_0
- tk=8.5.18=0
- traitlets=4.3.2=py27_0
- typing=3.6.2=py27_0
- wcwidth=0.1.7=py27_0
- wheel=0.29.0=py27_0
- xz=5.2.3=0
- yaml=0.1.6=0
- zlib=1.2.11=0
- bob=3.0.0=np112py27_0
- bob-extras=2017.10.22=0
- bob.ap=2.1.5=np112py27_0
- bob.bio.base=3.2.1=py_0
- bob.bio.caffe_face=1.1.0=py_0
- bob.bio.face=3.3.0=py_0
- bob.bio.gmm=3.1.0=py_0
- bob.bio.spear=3.1.1=py_0
- bob.bio.vein=1.0.1=py27_0
- bob.bio.video=3.3.0=py_0
- bob.blitz=2.0.14=np112py27_0
- bob.core=2.1.9=np112py27_0
- bob.db.arface=2.1.4=py_0
- bob.db.asvspoof=1.1.7=py_0
- bob.db.asvspoof2017=1.0.3=py_0
- bob.db.atnt=2.0.10=py27_0
- bob.db.atvskeystroke=2.1.5=py_0
- bob.db.avspoof=2.2.8=py_0
- bob.db.banca=2.1.5=py_0
- bob.db.base=2.3.2=py27_0
- bob.db.biosecure=2.1.5=py_0
- bob.db.biosecurid.face=2.1.8=py_0
- bob.db.casme2=2.1.5=py_0
- bob.db.caspeal=2.1.5=py_0
- bob.db.cohface=1.0.4=py_0
- bob.db.cuhk_cufs=2.1.3=py_0
- bob.db.frgc=2.1.3=py_0
- bob.db.fv3d=1.0.0=py_0
- bob.db.gbu=2.1.4=py_0
- bob.db.hci_tagging=1.0.4=py_0
- bob.db.ijba=2.0.3=py_0
- bob.db.iris=2.1.1=py27_0
- bob.db.kboc16=2.0.11=py_0
- bob.db.lfw=2.1.5=py_0
- bob.db.livdet2013=2.0.6=py_0
- bob.db.mnist=2.1.1=py27_0
- bob.db.mobio=2.1.5=py_0
- bob.db.msu_mfsd_mod=2.2.3=py_0
- bob.db.multipie=2.1.4=py_0
- bob.db.nist_sre12=3.0.3=py_0
- bob.db.pericrosseye=1.0.1=py_0
- bob.db.putvein=1.2.1=py_0
- bob.db.replay=3.0.3=py_0
- bob.db.replaymobile=1.1.3=py_0
- bob.db.scface=2.1.5=py_0
- bob.db.utfvp=2.4.1=py_0
- bob.db.verafinger=1.0.0=py_0
- bob.db.voicepa=1.0.2=py_0
- bob.db.voxforge=2.2.0=py_0
- bob.db.wine=2.0.9=py27_0
- bob.db.xm2vts=2.1.4=py_0
- bob.db.youtube=2.1.4=py_0
- bob.extension=2.4.5=py27_0
- bob.io.audio=2.0.6=np112py27_0
- bob.io.base=3.0.2=np112py27_0
- bob.io.image=2.3.0=np112py27_0
- bob.io.matlab=2.0.10=np112py27_0
- bob.io.video=2.0.19=np112py27_0
- bob.ip.base=2.1.0=np112py27_0
- bob.ip.caffe_extractor=1.1.1=py_0
- bob.ip.color=2.0.10=np112py27_0
- bob.ip.dlib=1.0.1=py27_0
- bob.ip.draw=2.0.9=np112py27_0
- bob.ip.facedetect=2.1.4=np112py27_0
- bob.ip.facelandmarks=1.0.4=py_0
- bob.ip.flandmark=2.1.6=np112py27_0
- bob.ip.gabor=2.0.11=np112py27_0
- bob.ip.optflow.hornschunck=2.0.13=np112py27_0
- bob.ip.optflow.liu=2.0.12=np112py27_0
- bob.ip.qualitymeasure=1.0.5=np112py27_0
- bob.ip.skincolorfilter=0.0.3=py_0
- bob.kaldi=1.1.0=py_0
- bob.learn.activation=2.0.10=np112py27_0
- bob.learn.boosting=2.0.13=np112py27_0
- bob.learn.em=2.1.0=np112py27_0
- bob.learn.libsvm=2.0.13=np112py27_0
- bob.learn.linear=2.1.2=np112py27_0
- bob.learn.mlp=2.1.6=np112py27_0
- bob.math=3.0.0=np112py27_0
- bob.measure=2.4.1=np112py27_0
- bob.pad.base=1.0.8=py_0
- bob.pad.voice=1.0.4=py_0
- bob.sp=2.0.10=np112py27_0
- cyvlfeat=0.4.6=np112py27_2
- dlib=19.0=np112py27_1
- ffmpeg=2.8.10=4
- giflib=5.1.4=3
- gridtk=1.4.4=py_0
- kaldi=r7271.1a4dbf6=0
- libblitz=0.10=3
- libmatio=1.5.6=3
- libogg=1.3.2=0
- libsvm=3.21=3
- menpo=0.8.1=np112py27_0
- menpofit=0.5.0=py27_0
- mne=0.14.1=np112py27_0
- opencv=3.1.0=np112py27_4
- openfst=1.6.1=0
- pathlib=1.0=py27_0
- pyedflib=0.1.9=np112py27_0
- schema=0.6.5=py27_0
- sox=14.4.2=5
- speex=1.2.0=0
- vlfeat=0.9.20=3
- x264=20131217=3
- zc.buildout
- pip:
- backports.shutil-get-terminal-size==1.0.0
- bob.db.cuhk-cufs==2.1.3
- bob.db.hci-tagging==1.0.4
- bob.db.msu-mfsd-mod==2.2.3
- bob.db.nist-sre12==3.0.3
- ipython-genutils==0.2.0
- prompt-toolkit==1.0.15
\ No newline at end of file
requirements.txt 0 → 100644
+ 12
− 0
View file @ c9eb6ed3
# SPDX-FileCopyrightText: © 2018 Idiap Research Institute <contact@idiap.ch>
# SPDX-FileContributor: Vedrana Krivokuca Hahn <vkrivokuca@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-only
bob.bio.vein
scipy
bob.io.base
bob.measure
numpy
matplotlib
\ No newline at end of file
setup.py 0 → 100644
+ 94
− 0
View file @ c9eb6ed3
#!/usr/bin/env python
# SPDX-FileCopyrightText: © 2018 Idiap Research Institute <contact@idiap.ch>
# SPDX-FileContributor: Vedrana Krivokuca Hahn <vkrivokuca@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-only
# vim: set fileencoding=utf-8 :
from setuptools import setup, dist
dist.Distribution(dict(setup_requires = ['bob.extension']))
# load the requirements.txt for additional requirements
from bob.extension.utils import load_requirements, find_packages
install_requires = load_requirements()
# The only thing we do in this file is to call the setup() function with all
# parameters that define our package.
setup(
# This is the basic information about your project. Modify all this
# information before releasing code publicly.
name = 'bob.paper.isba2018_entropy',
version = '1.0.3',
description = 'Calculating the entropy of fingervein patterns',
url = 'https://gitlab.idiap.ch/bob/bob.paper.isba2018_entropy',
license = 'GPLv3',
author = 'Vedrana Krivokuca',
author_email = 'vedrana.krivokuca@idiap.ch',
keywords = 'bob, fingervein recognition, entropy',
# If you have a better, long description of your package, place it on the
# 'doc' directory and then hook it here
long_description = open('README.rst').read(),
# This line is required for any distutils based packaging.
# It will find all package-data inside the 'bob' directory.
packages = find_packages('bob'),
include_package_data = True,
# This line defines which packages should be installed when you "install"
# this package. All packages that are mentioned here, but are not installed
# on the current system will be installed locally and only visible to the
# scripts of this package. Don't worry - You won't need administrative
# privileges when using buildout.
install_requires = install_requires,
# This entry defines which scripts you will have inside the 'bin' directory
# once you install the package (or run 'bin/buildout'). The order of each
# entry under 'console_scripts' is like this:
# script-name-at-bin-directory = module.at.your.library:function
#
# The module.at.your.library is the python file within your library, using
# the python syntax for directories (i.e., a '.' instead of '/' or '\').
# This syntax also omits the '.py' extension of the filename. So, a file
# installed under 'example/foo.py' that contains a function which
# implements the 'main()' function of particular script you want to have
# should be referred as 'example.foo:main'.
#
# In this simple example we will create a single program that will print
# the version of bob.
entry_points = {
# configurations should be declared using this entry:
'bob.bio.config': [
'utfvp-mc = bob.paper.isba2018_entropy.configurations.utfvp_mc', # MC extractor for images from UTFVP database
'utfvp-rlt = bob.paper.isba2018_entropy.configurations.utfvp_rlt', # RLT extractor for images from UTFVP database
'utfvp-wld = bob.paper.isba2018_entropy.configurations.utfvp_wld', # WLD extractor for images from UTFVP database
'vera-mc = bob.paper.isba2018_entropy.configurations.vera_mc', # MC extractor for images from VERA database
'vera-rlt = bob.paper.isba2018_entropy.configurations.vera_rlt', # RLT extractor for images from VERA database
'vera-wld = bob.paper.isba2018_entropy.configurations.vera_wld', # WLD extractor for images from VERA database
],
# scripts should be declared using this entry:
'console_scripts' : [
'calculate_entropy.py = bob.paper.isba2018_entropy.scripts.calculate_entropy:main', # Script that calculates entropy of fingervein patterns
],
},
# Classifiers are important if you plan to distribute this package through
# PyPI. You can find the complete list of classifiers that are valid and
# useful here (http://pypi.python.org/pypi?%3Aaction=list_classifiers).
classifiers = [
'Framework :: Bob',
'Development Status :: 3 - Alpha',
'Intended Audience :: Developers',
'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
'Natural Language :: English',
'Programming Language :: Python',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
],
)
\ No newline at end of file
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