def __init__(self, lbp_operator=ExtendedLBP(), sz=(8, 8)):
AbstractFeature.__init__(self)
if not isinstance(lbp_operator, LocalDescriptor):
raise TypeError(
"Only an operator of type facerec.lbp.LocalDescriptor is a valid lbp_operator."
)
self.lbp_operator = lbp_operator
self.sz = sz
def __init__(self, lbp_operator = ExtendedLBP(radius=1, neighbors=8)):
AbstractFeature.__init__(self)
self._lbp_operator = lbp_operator
# Define filters for the Dataset:
yale_subset_0_40 = YaleBaseFilter(0, 40, 0, 40)
# Now read in the image data. Apply filters, scale to 128 x 128 pixel:
[X, y] = read_images(sys.argv[1], yale_subset_0_40, sz=(64, 64))
# Set up a handler for logging:
handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
# Add handler to facerec modules, so we see what's going on inside:
logger = logging.getLogger("facerec")
logger.addHandler(handler)
logger.setLevel(logging.INFO)
# The models we want to evaluate:
model0 = PredictableModel(
feature=SpatialHistogram(lbp_operator=ExtendedLBP()),
classifier=NearestNeighbor(dist_metric=ChiSquareDistance(), k=1))
model1 = PredictableModel(feature=SpatialHistogram(lbp_operator=LPQ()),
classifier=NearestNeighbor(
dist_metric=ChiSquareDistance(), k=1))
# The sigmas we'll apply for each run:
sigmas = [0]
print('The experiment will be run %s times!' % ITER_MAX)
# Initialize experiments (with empty results):
experiments = {}
experiments['lbp_model'] = {
'model': model0,
'results': {},
'color': 'r',
'linestyle': '--',
'marker': '*'
print "USAGE: lpq_experiment.py </path/to/images>"
sys.exit()
# Define filters for the Dataset:
yale_subset_0_40 = YaleBaseFilter(0, 40, 0, 40)
# Now read in the image data. Apply filters, scale to 128 x 128 pixel:
[X,y] = read_images(sys.argv[1], yale_subset_0_40, sz=(64,64))
# Set up a handler for logging:
handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
# Add handler to facerec modules, so we see what's going on inside:
logger = logging.getLogger("facerec")
logger.addHandler(handler)
logger.setLevel(logging.INFO)
# The models we want to evaluate:
model0 = PredictableModel(feature=SpatialHistogram(lbp_operator=ExtendedLBP()), classifier=NearestNeighbor(dist_metric=ChiSquareDistance(), k=1))
model1 = PredictableModel(feature=SpatialHistogram(lbp_operator=LPQ()), classifier=NearestNeighbor(dist_metric=ChiSquareDistance(), k=1))
# The sigmas we'll apply for each run:
sigmas = [0]
print 'The experiment will be run %s times!' % ITER_MAX
# Initialize experiments (with empty results):
experiments = {}
experiments['lbp_model'] = { 'model': model0, 'results' : {}, 'color' : 'r', 'linestyle' : '--', 'marker' : '*'}
experiments['lpq_model'] = { 'model': model1, 'results' : {}, 'color' : 'b', 'linestyle' : '--', 'marker' : 's'}
# Loop to acquire the results for each experiment:
for sigma in sigmas:
print "Setting sigma=%s" % sigma
for key, value in experiments.iteritems():
print 'Running experiment for model=%s' % key
# Define the validators for the model:
cv0 = SimpleValidation(value['model'])