def generate_test_vectors():
vs = vectors()
vs.append(vector([0, 1, 2]))
vs.append(vector([3, 4, 5]))
vs.append(vector([6, 7, 8]))
assert len(vs) == 3
return vs
def train(tagged):
"""
Trains an SVM classifier based on the training data passed.
Mostly based on http://dlib.net/svm_binary_classifier.py.html.
:param tagged: list of TaggedFace to train on
:return: dlib.svm
"""
x = dlib.vectors() # will carry the facial encodings
y = dlib.array() # will carry the gender label
print("Preparing dataset...")
total = len(tagged)
for i, t in enumerate(tagged):
print(f"\rEncoding {t.path} ({i + 1}/{total})...", end="")
faces = encode(t.img)
x.append(dlib.vector(faces[0]))
y.append(t.tag)
img = t.img
for _ in range(5):
faces = encode(img)
if not faces:
break
x.append(dlib.vector(faces[0]))
y.append(t.tag)
img = cv2.resize(img, None, fx=0.7, fy=0.7)
print("Training SVM...")
trainer = dlib.svm_c_trainer_radial_basis()
#trainer.be_verbose()
trainer.set_c(10)
model = trainer.train(x, y)
with open(PATH_SVMFILE, "wb") as filehandle:
pickle.dump(model, filehandle)
return None
def sentence_to_vectors(sentence):
# Create an empty array of vectors
vects = dlib.vectors()
for word in sentence.split():
# Our vectors are very simple 1-dimensional vectors. The value of the single
# feature is 1 if the first letter of the word is capitalized and 0 otherwise.
if (word[0].isupper()):
vects.append(dlib.vector([1]))
else:
vects.append(dlib.vector([0]))
return vects
def sentence_to_vectors(sentence):
# Create an empty array of vectors
vects = dlib.vectors()
for word in sentence.split():
# Our vectors are very simple 1-dimensional vectors. The value of the single
# feature is 1 if the first letter of the word is capitalized and 0 otherwise.
if (word[0].isupper()):
vects.append(dlib.vector([1]))
else:
vects.append(dlib.vector([0]))
return vects
def training_data():
r = Random(0)
predictors = vectors()
sparse_predictors = sparse_vectors()
response = array()
for i in range(30):
for c in [-1, 1]:
response.append(c)
values = [r.random() + c * 0.5 for _ in range(3)]
predictors.append(vector(values))
sp = sparse_vector()
for i, v in enumerate(values):
sp.append(pair(i, v))
sparse_predictors.append(sp)
return predictors, sparse_predictors, response
def training_data():
r = Random(0)
predictors = vectors()
sparse_predictors = sparse_vectors()
response = array()
for i in range(30):
for c in [-1, 1]:
response.append(c)
values = [r.random() + c * 0.5 for _ in range(3)]
predictors.append(vector(values))
sp = sparse_vector()
for i, v in enumerate(values):
sp.append(pair(i, v))
sparse_predictors.append(sp)
return predictors, sparse_predictors, response
def cluster():
s = time.time()
query = ''
descriptors = []
dvec = dlib.vectors()
date = input("enter a date in dd-mm-yyy format")
from_time = input("enter start time in hh:mm format")
to_time = input("enter end time in hh:mm format")
data = ptf.retrive(date, from_time, to_time)
for d in data:
descriptors.append(dlib.vector(d))
# Cluster the faces.
labels = dlib.chinese_whispers_clustering(descriptors, 0.5)
e = time.time()
print(labels)
print(len(descriptors))
print(len(labels))
labset = set(labels)
print(labset)
num_classes = len(set(labels)) #total number of clusters
print("Number of clusters: {}".format(num_classes))
print(e - s)
return num_classes
# root folder and run:
# python setup.py install
#
# Compiling dlib should work on any operating system so long as you have
# CMake installed. On Ubuntu, this can be done easily by running the
# command:
# sudo apt-get install cmake
#
import dlib
try:
import cPickle as pickle
except ImportError:
import pickle
x = dlib.vectors()
y = dlib.array()
# Make a training dataset. Here we have just two training examples. Normally
# you would use a much larger training dataset, but for the purpose of example
# this is plenty. For binary classification, the y labels should all be either +1 or -1.
x.append(dlib.vector([1, 2, 3, -1, -2, -3]))
y.append(+1)
x.append(dlib.vector([-1, -2, -3, 1, 2, 3]))
y.append(-1)
# Now make a training object. This object is responsible for turning a
# training dataset into a prediction model. This one here is a SVM trainer
# that uses a linear kernel. If you wanted to use a RBF kernel or histogram
# intersection kernel you could change it to one of these lines:
def test_vectors_extend():
vs = vectors()
vs.extend([vector([1, 2, 3]), vector([4, 5, 6])])
assert len(vs) == 2
def test_vectors_resize():
vs = vectors()
vs.resize(100)
assert len(vs) == 100
for i in range(100):
assert len(vs[i]) == 0
# if you have a CPU that supports AVX instructions, since this makes some
# things run faster.
#
# Compiling dlib should work on any operating system so long as you have
# CMake and boost-python installed. On Ubuntu, this can be done easily by
# running the command:
# sudo apt-get install libboost-python-dev cmake
#
import dlib
try:
import cPickle as pickle
except ImportError:
import pickle
x = dlib.vectors()
y = dlib.array()
# Make a training dataset. Here we have just two training examples. Normally
# you would use a much larger training dataset, but for the purpose of example
# this is plenty. For binary classification, the y labels should all be either +1 or -1.
x.append(dlib.vector([1, 2, 3, -1, -2, -3]))
y.append(+1)
x.append(dlib.vector([-1, -2, -3, 1, 2, 3]))
y.append(-1)
# Now make a training object. This object is responsible for turning a
# training dataset into a prediction model. This one here is a SVM trainer
# that uses a linear kernel. If you wanted to use a RBF kernel or histogram
