class PredictableModelWrapper(object):
def __init__(self, model):
self.model = model
self.numeric_dataset = NumericDataSet()
def compute(self):
X,y = self.numeric_dataset.get()
self.model.compute(X,y)
def set_data(self, numeric_dataset):
self.numeric_dataset = numeric_dataset
def predict(self, image):
prediction_result = self.model.predict(image)
# Only take label right now:
num_label = prediction_result[0]
str_label = self.numeric_dataset.resolve_by_num(num_label)
return str_label
def update(self, name, image):
self.numeric_dataset.add(name, image)
class_label = self.numeric_dataset.resolve_by_str(name)
extracted_feature = self.feature.extract(image)
self.classifier.update(extracted_feature, class_label)
def __repr__(self):
return "PredictableModelWrapper (Inner Model=%s)" % (str(self.model))
class PredictableModelWrapper(object):
def __init__(self, model):
self.model = model
self.numeric_dataset = NumericDataSet()
def compute(self):
X,y = self.numeric_dataset.get()
self.model.compute(X,y)
def set_data(self, numeric_dataset):
self.numeric_dataset = numeric_dataset
def predict(self, image):
prediction_result = self.model.predict(image)
# Only take label right now:
num_label = prediction_result[0]
str_label = self.numeric_dataset.resolve_by_num(num_label)
return str_label
def update(self, name, image):
self.numeric_dataset.add(name, image)
class_label = self.numeric_dataset.resolve_by_str(name)
extracted_feature = self.feature.extract(image)
self.classifier.update(extracted_feature, class_label)
def __repr__(self):
return "PredictableModelWrapper (Inner Model=%s)" % (str(self.model))
def read_from_file(image_paths, identifier):
numeric_dataset = NumericDataSet()
if not isinstance(image_paths, list):
image_paths = [image_paths]
for path in image_paths:
read_image(path, identifier, numeric_dataset)
return numeric_dataset
def read_from_csv(filename):
numeric_dataset = NumericDataSet()
with open(filename, 'rb') as csvfile:
reader = csv.reader(csvfile, delimiter=';', quotechar='#')
for row in reader:
identifier = row[0]
path = row[1]
read_images(path, identifier, numeric_dataset)
return numeric_dataset
def read_from_folder(folder_path):
numeric_dataset = NumericDataSet()
if os.path.isdir(folder_path):
for subdir, dirs, files in os.walk(folder_path):
for file in files:
file_path = os.path.abspath("%s/%s"% (subdir, file))
sub_dir_parts = os.path.abspath(subdir).split("\\")
identifier = subdir
if sub_dir_parts:
identifier = sub_dir_parts[-1]
read_image(file_path, identifier, numeric_dataset)
return numeric_dataset
def __init__(self, model):
self.model = model
self.numeric_dataset = NumericDataSet()
def __init__(self, model):
self.model = model
self.numeric_dataset = NumericDataSet()
import numpy as np
# import matplotlib colormaps
import matplotlib.cm as cm
# import for logging
import logging, sys
# 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
logger = logging.getLogger("facerec")
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
# load a dataset (e.g. AT&T Facedatabase)
dataSet = NumericDataSet("/home/philipp/facerec/data/yalefaces_recognition")
# define Fisherfaces as feature extraction method
feature = Fisherfaces()
# define a 1-NN classifier with Euclidean Distance
classifier = NearestNeighbor(dist_metric=EuclideanDistance(), k=1)
# define the model as the combination
model = PredictableModel(feature=feature, classifier=classifier)
# show fisherfaces
model.compute(dataSet.data, dataSet.labels)
# turn the first (at most) 16 eigenvectors into grayscale
# images (note: eigenvectors are stored by column!)
E = []
for i in xrange(min(model.feature.eigenvectors.shape[1], 16)):
e = model.feature.eigenvectors[:, i].reshape(dataSet.data[0].shape)
E.append(minmax_normalize(e, 0, 255, dtype=np.uint8))
# plot them and store the plot to "python_fisherfaces_fisherfaces.pdf"
