diff --git a/buildout.cfg b/buildout.cfg
index d86bf8ca3f842c0ba52bf085254c0dca0f010a4c..aa737a64ad2cd4019426d2b70ed8fbcedb00cbc8 100644
--- a/buildout.cfg
+++ b/buildout.cfg
@@ -13,13 +13,18 @@ eggs = sphinx
bob
bob.example.faceverify
+; This points to the bob databases that are installed locally at Idiap
+; TODO: remove this as soon as the databases are published at pypi
+find-links = http://www.idiap.ch/software/bob/packages/xbob/nightlies/last
+
; This defines the installation directory of Bob.
; The current setup should work fine for use at Idiap.
; If you are not at Idiap, and bob is not installed in the default location,
; please modify the egg-directories accordingly.
+; TODO: Change this directory back to the stable version of bob, when appropriate
[external]
recipe = xbob.buildout:external
-egg-directories = /idiap/group/torch5spro/releases/bob-1.0.5/install/linux-x86_64-release/lib
+egg-directories = /idiap/group/torch5spro/nightlies/last/install/linux-x86_64-release/lib
[python]
diff --git a/faceverify/dct_ubm.py b/faceverify/dct_ubm.py
index a2c1a38e205509db649b3eff10eb4e15875cd052..20c5210da6e98b5cbd994330c9fd1845ad33e3f5 100644
--- a/faceverify/dct_ubm.py
+++ b/faceverify/dct_ubm.py
@@ -1,4 +1,5 @@
import bob
+import xbob.db.atnt
import os, sys
import numpy
from matplotlib import pyplot
@@ -38,15 +39,15 @@ def extract_feature(image):
# compute shape of the image blocks
block_shape = bob.ip.get_block_shape(image, DCT_BLOCK_SIZE, DCT_BLOCK_SIZE, DCT_BLOCK_OVERLAP, DCT_BLOCK_OVERLAP)
image_blocks = numpy.ndarray(block_shape, 'float64')
-
+
# fill image blocks
bob.ip.block(image, image_blocks, DCT_BLOCK_SIZE, DCT_BLOCK_SIZE, DCT_BLOCK_OVERLAP, DCT_BLOCK_OVERLAP)
# perform DCT on image blocks
dct_blocks = dct_extractor(image_blocks)
-
+
return dct_blocks
-
+
# Parameters of the UBM/GMM module training
@@ -72,7 +73,7 @@ def train(training_features):
# train using the KMeansTrainer
kmeans_trainer.train(kmeans, training_set)
-
+
[variances, weights] = kmeans.get_variances_and_weights_for_each_cluster(training_set)
means = kmeans.means
@@ -100,7 +101,7 @@ def enrol(model_features, ubm, gmm_trainer):
# train the GMM
gmm_trainer.train(gmm, enrol_set)
- # return the resulting gmm
+ # return the resulting gmm
return gmm
@@ -108,21 +109,21 @@ def stats(probe_feature, ubm):
"""Computes the UBM Statistics for the given feature vector"""
# compute the UBM stats for the given probe feature
probe_feature = bob.io.Arrayset(probe_feature)
-
+
# Accumulate statistics
gmm_stats = bob.machine.GMMStats(ubm.dim_c, ubm.dim_d)
gmm_stats.init()
ubm.acc_statistics(probe_feature, gmm_stats)
-
+
return gmm_stats
-
+
def main():
"""This function will perform an a DCT block extraction and a UBM/GMM modeling test on the AT&T database"""
-
+
# use the bob.db interface to retrieve information about the Database
- atnt_db = bob.db.atnt.Database()
-
+ atnt_db = xbob.db.atnt.Database()
+
# check if the AT&T database directory is overwritten by the command line
global ATNT_IMAGE_DIRECTORY
if len(sys.argv) > 1:
@@ -132,12 +133,12 @@ def main():
if not os.path.isdir(ATNT_IMAGE_DIRECTORY):
print "The database directory '" + ATNT_IMAGE_DIRECTORY + "' does not exists!"
return
-
+
#####################################################################
- ### UBM Training
+ ### UBM Training
# load all training images
- training_images = load_images(atnt_db, group = 'train')
-
+ training_images = load_images(atnt_db, group = 'world')
+
print "Extracting training features"
training_features = {}
for key, image in training_images.iteritems():
@@ -145,20 +146,20 @@ def main():
print "Training UBM model"
ubm = train(training_features)
-
+
#####################################################################
### GMM model enrollment
print "Enrolling GMM models"
gmm_trainer = bob.trainer.MAP_GMMTrainer()
gmm_trainer.max_iterations = 1
gmm_trainer.set_prior_gmm(ubm)
-
+
# create a GMM model for each model identity
- model_ids = atnt_db.client_ids(groups = 'test')
+ model_ids = atnt_db.client_ids(groups = 'dev')
models = {}
for model_id in model_ids:
# load images for the current model id
- model_images = load_images(atnt_db, group = 'test', purpose = 'enrol', client_id = model_id)
+ model_images = load_images(atnt_db, group = 'dev', purpose = 'enrol', client_id = model_id)
models_for_current_id = {}
# extract model features
for key, image in model_images.iteritems():
@@ -166,12 +167,12 @@ def main():
# enroll model for the current identity from these features
model = enrol(models_for_current_id, ubm, gmm_trainer)
models[model_id] = model
-
+
#####################################################################
### probe stats
-
+
print "Computing probe statistics"
- probe_images = load_images(atnt_db, group = 'test', purpose = 'probe')
+ probe_images = load_images(atnt_db, group = 'dev', purpose = 'probe')
probes = {}
for key, image in probe_images.iteritems():
# extract probe features
@@ -183,7 +184,7 @@ def main():
### compute scores, we here choose a simple Euclidean distance measure
positive_scores = []
negative_scores = []
-
+
print "Computing scores"
distance_function = bob.machine.linear_scoring
@@ -192,24 +193,24 @@ def main():
for probe_key, probe_stats in probes.iteritems():
# compute score
score = distance_function([model_gmm], ubm, [probe_stats])[0,0]
-
+
# check if this is a positive score
if model_id == atnt_db.get_client_id_from_file_id(probe_key):
positive_scores.append(score)
else:
negative_scores.append(score)
-
+
print "Evaluation"
# convert list of scores to numpy arrays
positives = numpy.array(positive_scores)
negatives = numpy.array(negative_scores)
-
+
# compute equal error rate
threshold = bob.measure.eer_threshold(negatives, positives)
FAR, FRR = bob.measure.farfrr(negatives, positives, threshold)
-
+
print "Result: FAR", FAR, "and FRR", FRR, "at threshold", threshold
-
+
# plot ROC curve
bob.measure.plot.roc(negatives, positives)
pyplot.xlabel("False Rejection Rate (%)")
@@ -217,11 +218,11 @@ def main():
pyplot.title("ROC Curve for UBM/GMM based AT&T Verification Experiment")
pyplot.grid()
pyplot.axis([0, 100, 0, 100]) #xmin, xmax, ymin, ymax
-
+
# save plot to file
pyplot.savefig("dct_ubm.png")
# show ROC curve.
# enable it if you like. This will open a window and display the ROC curve
-# pyplot.show()
-
+# pyplot.show()
+
diff --git a/faceverify/eigenface.py b/faceverify/eigenface.py
index 0a08784743650c18ec641b9e53b4f808ee5c54da..18491b1f245745e5a28af26d334877152ae4fb81 100644
--- a/faceverify/eigenface.py
+++ b/faceverify/eigenface.py
@@ -1,4 +1,5 @@
import bob
+import xbob.db.atnt
import os, sys
import numpy
from matplotlib import pyplot
@@ -22,7 +23,7 @@ def load_images(db, group = None, purpose = None):
# load image and linearize it into a vector
images[key] = bob.io.load(image_name).astype(numpy.float64)
return images
-
+
# The number of eigenfaces that should be kept
KEPT_EIGENFACES = 5
@@ -31,41 +32,41 @@ def train(training_images):
"""Trains the PCA module with the given list of training images"""
# perform training using a SVD PCA trainer
pca_trainer = bob.trainer.SVDPCATrainer()
-
+
# create array set used for training
training_set = bob.io.Arrayset()
-
+
# iterate through the training examples and linearize the images
for image in training_images.values():
training_set.append(image.flatten())
# training the SVD PCA returns a machine that can be used for projection
pca_machine, eigen_values = pca_trainer.train(training_set)
-
+
# limit the number of kept eigenfaces
pca_machine.resize(pca_machine.shape[0], KEPT_EIGENFACES)
-
+
return pca_machine
-
+
def extract_feature(image, pca_machine):
"""Projects the given list of images to the PCA subspace and returns the results"""
# create projection result in the desired size
projected_feature = numpy.ndarray((KEPT_EIGENFACES,), dtype = numpy.float64)
-
+
# project the data after linearizing them
pca_machine(image.flatten(), projected_feature)
-
+
# return the projected data
return projected_feature
def main():
"""This function will perform an eigenface test on the AT&T database"""
-
+
# use the bob.db interface to retrieve information about the Database
- atnt_db = bob.db.atnt.Database()
-
+ atnt_db = xbob.db.atnt.Database()
+
# check if the AT&T database directory is overwritten by the command line
global ATNT_IMAGE_DIRECTORY
if len(sys.argv) > 1:
@@ -75,13 +76,13 @@ def main():
if not os.path.isdir(ATNT_IMAGE_DIRECTORY):
print "The database directory '" + ATNT_IMAGE_DIRECTORY + "' does not exists!"
return
-
+
#####################################################################
### Training
-
+
# load all training images
- training_images = load_images(atnt_db, group = 'train')
-
+ training_images = load_images(atnt_db, group = 'world')
+
print "Training PCA machine"
pca_machine = train(training_images)
@@ -89,9 +90,9 @@ def main():
### extract eigenface features of model and probe images
# load model and probe images
- model_images = load_images(atnt_db, group = 'test', purpose = 'enrol')
- probe_images = load_images(atnt_db, group = 'test', purpose = 'probe')
-
+ model_images = load_images(atnt_db, group = 'dev', purpose = 'enrol')
+ probe_images = load_images(atnt_db, group = 'dev', purpose = 'probe')
+
print "Extracting models"
model_features = {}
for key, image in model_images.iteritems():
@@ -100,13 +101,13 @@ def main():
probe_features = {}
for key, image in probe_images.iteritems():
probe_features[key] = extract_feature(image, pca_machine)
-
+
#####################################################################
### compute scores, we here choose a simple Euclidean distance measure
positive_scores = []
negative_scores = []
-
+
print "Computing scores"
distance_function = bob.math.euclidean_distance
@@ -115,24 +116,24 @@ def main():
for probe_key, probe_feature in probe_features.iteritems():
# compute score
score = distance_function(model_feature, probe_feature)
-
+
# check if this is a positive score
if atnt_db.get_client_id_from_file_id(model_key) == atnt_db.get_client_id_from_file_id(probe_key):
positive_scores.append(score)
else:
negative_scores.append(score)
-
+
print "Evaluation"
# convert list of scores to numpy arrays
positives = numpy.array(positive_scores)
negatives = numpy.array(negative_scores)
-
+
# compute equal error rate
threshold = bob.measure.eer_threshold(negatives, positives)
FAR, FRR = bob.measure.farfrr(negatives, positives, threshold)
-
+
print "Result: FAR", FAR, "and FRR", FRR, "at threshold", threshold
-
+
# plot ROC curve
bob.measure.plot.roc(negatives, positives)
pyplot.xlabel("False Rejection Rate (%)")
@@ -141,10 +142,10 @@ def main():
pyplot.grid()
pyplot.axis([0, 100, 0, 100]) #xmin, xmax, ymin, ymax
- # save plot to file
+ # save plot to file
pyplot.savefig("eigenface.png")
# show ROC curve.
# enable it if you like. This will open a window and display the ROC curve
-# pyplot.show()
-
+# pyplot.show()
+
diff --git a/faceverify/gabor_phase.py b/faceverify/gabor_phase.py
index f82d2b7abf7aaeaf40bb34d564331311a9e0cca0..3cc94519fd3c9dac181e0e6ce838068b1bc94a7b 100644
--- a/faceverify/gabor_phase.py
+++ b/faceverify/gabor_phase.py
@@ -1,4 +1,5 @@
import bob
+import xbob.db.atnt
import os, sys
import numpy
from matplotlib import pyplot
@@ -22,7 +23,7 @@ def load_images(db, group = None, purpose = None):
# load image and linearize it into a vector
images[key] = bob.io.load(image_name).astype(numpy.float64)
return images
-
+
# define Gabor wavelet transform class globally since it is reused for all images
gabor_wavelet_transform = bob.ip.GaborWaveletTransform()
@@ -37,22 +38,22 @@ def extract_feature(image, graph_machine):
# add the shape of one Gabor jet
shape.extend(jet_image[0,0].shape)
gabor_graph = numpy.ndarray(shape, dtype = numpy.float64)
-
+
# perform Gabor wavelet transform on the image
gabor_wavelet_transform.compute_jets(image, jet_image)
-
+
# extract the Gabor graphs from the feature image
graph_machine(jet_image, gabor_graph)
-
+
# return the extracted graph
return gabor_graph
def main():
"""This function will perform Gabor graph comparison test on the AT&T database"""
-
+
# use the bob.db interface to retrieve information about the Database
- atnt_db = bob.db.atnt.Database()
+ atnt_db = xbob.db.atnt.Database()
# check if the AT&T database directory is overwritten by the command line
global ATNT_IMAGE_DIRECTORY
@@ -63,22 +64,22 @@ def main():
if not os.path.isdir(ATNT_IMAGE_DIRECTORY):
print "The database directory '" + ATNT_IMAGE_DIRECTORY + "' does not exists!"
return
-
+
#####################################################################
### Training
-
- # for Gabor graphs, no training is required.
-
+
+ # for Gabor graphs, no training is required.
+
print "Creating Gabor graph machine"
# create a machine that will produce tight Gabor graphs with inter-node distance (1,1)
graph_machine = bob.machine.GaborGraphMachine((0,0), (111,91), (1,1))
-
+
#####################################################################
### extract Gabor graph features for all model and probe images
# load all model and probe images
- model_images = load_images(atnt_db, group = 'test', purpose = 'enrol')
- probe_images = load_images(atnt_db, group = 'test', purpose = 'probe')
-
+ model_images = load_images(atnt_db, group = 'dev', purpose = 'enrol')
+ probe_images = load_images(atnt_db, group = 'dev', purpose = 'probe')
+
print "Extracting models"
model_features = {}
for key, image in model_images.iteritems():
@@ -87,17 +88,17 @@ def main():
probe_features = {}
for key, image in probe_images.iteritems():
probe_features[key] = extract_feature(image, graph_machine)
-
+
#####################################################################
### compute scores, we here choose a simple Euclidean distance measure
positive_scores = []
negative_scores = []
-
+
print "Computing scores"
# define a certain Gabor jet similarity function that should be used
similarity_function = bob.machine.DisparityCorrectedPhaseDifference()
-
+
# iterate through models and probes and compute scores
for model_key, model_feature in model_features.iteritems():
for probe_key, probe_feature in probe_features.iteritems():
@@ -109,16 +110,16 @@ def main():
positive_scores.append(score)
else:
negative_scores.append(score)
-
+
print "Evaluation"
# convert list of scores to numpy arrays
positives = numpy.array(positive_scores)
negatives = numpy.array(negative_scores)
-
+
# compute equal error rate
threshold = bob.measure.eer_threshold(negatives, positives)
FAR, FRR = bob.measure.farfrr(negatives, positives, threshold)
-
+
print "Result: FAR", FAR, "and FRR", FRR, "at threshold", threshold
# plot ROC curve
@@ -129,10 +130,10 @@ def main():
pyplot.grid()
pyplot.axis([0, 100, 0, 100]) #xmin, xmax, ymin, ymax
- # save plot to file
+ # save plot to file
pyplot.savefig("gabor_phase.png")
# show ROC curve.
# enable it if you like. This will open a window and display the ROC curve
-# pyplot.show()
+# pyplot.show()
diff --git a/setup.py b/setup.py
index ed51d15944225b2fd3ec3d245e2b72fb3fb04687..1b1932533c9c2bf35c73e775fb6a9923e9fb5712 100644
--- a/setup.py
+++ b/setup.py
@@ -45,9 +45,12 @@ setup(
# 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.
+
+ # TODO: Add a version number requirement to bob, when ready
install_requires=[
"sphinx", # to generate the documentation
- "bob >= 1.0.0, < 1.1.0", # base signal proc./machine learning library
+ "bob", # base signal proc./machine learning library
+ "xbob.db.atnt", # the AT&T (ORL) database of images
],
# This entry defines which scripts you will have inside the 'bin' directory