def checkAllPeople(config, color="#ab1749"):
from sklearn.model_selection import train_test_split
import numpy as np
model = DatasetModel([config[0], config[1]])
model = model.labeledByFolderOfFiles(config[2])
m, n = model.getDim()
X, y = model.exportedAsClassicDataset()
pI = model.partitionIndexes
X0 = X[:pI[1]]
y0 = y[:pI[1]]
X_train, X_test1, y_train, y_test1 = train_test_split(
X0,
y0,
test_size=0.2, # 0.2
random_state=0,
stratify=y0)
X_test2 = X[pI[1]:]
y_test2 = y[pI[1]:]
print(X_test1.shape, X_test2.shape)
X_test = np.concatenate((X_test1, X_test2))
y_test = np.concatenate((y_test1, y_test2))
eigenfaceModel = EigenfaceModel(X_train,
y_train,
nmin=4,
nmax=150,
m=m,
n=n)
epsilons = zeros(len(y_test))
for i, X_i in enumerate(X_test):
Phi_i = X_i - eigenfaceModel.mean_face
Ω_i = eigenfaceModel.projectOntoFacespace(X_i)
epsilons[i] = sqrt(abs(Phi_i.T.dot(Phi_i) -
Ω_i.T.dot(Ω_i))) #sqrt(norm(X_i - Ω_i))
# 4 possibilities (from paper)
# (1) near face space and near a face class
# (2) near face space but not near to a known face class
# (3) distant from face space and near a face class
# (4) distant from face space and not near a known face class
plotHist(epsilons, "Distance between image and face-space",
"Distance Hist for faces that should be known", color)
print(max(epsilons))
sd = np.std(epsilons, ddof=1, dtype=np.float64)
mean = np.mean(epsilons)
print("mean:", mean)
print("sd:", sd)
print("suggested cut threshold (mean+2sd):", mean + 2 * sd)
return X_train, y_train, X_test, y_test, eigenfaceModel, model
def checkKnownPeople(config, color=blue_color):
from sklearn.model_selection import train_test_split
import numpy as np
#model = DatasetModel([output100lbp, output100lbp_test_strict])
model = DatasetModel(config[0])
model = model.labeledByFolderOfFiles(config[2])
m, n = model.getDim()
X, y = model.exportedAsClassicDataset()
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
test_size=0.2, # 0.2
random_state=0,
stratify=y)
eigenfaceModel = EigenfaceModel(X_train,
y_train,
nmin=4,
nmax=150,
m=m,
n=n)
epsilons = zeros(len(y_test))
for i, X_i in enumerate(X_test):
Phi_i = X_i - eigenfaceModel.mean_face
Ω_i = eigenfaceModel.projectOntoFacespace(X_i)
#Phi_f = zeros(len(Phi_i))
#Phi_f[:len(Ω_i)] = Ω_i
#epsilons[i] = norm(Phi_i - Phi_f)
epsilons[i] = sqrt(abs(Phi_i.T.dot(Phi_i) -
Ω_i.T.dot(Ω_i))) #sqrt(norm(X_i - Ω_i))
# 4 possibilities (from paper)
# (1) near face space and near a face class
# (2) near face space but not near to a known face class
# (3) distant from face space and near a face class
# (4) distant from face space and not near a known face class
plotHist(epsilons, "Distance between image and face-space",
"Distance Hist for faces that should be known")
print(max(epsilons))
sd = np.std(epsilons, ddof=1, dtype=np.float64)
mean = np.mean(epsilons)
print("mean:", mean)
print("sd:", sd)
print("suggested cut threshold (mean+2sd):", mean + 2 * sd)
return X_train, y_train, X_test, y_test, eigenfaceModel, model
def checkUnknownPeople(config, color=pink_color):
model = DatasetModel([config[0], config[1]])
model = model.labeledByFolderOfFiles(config[2])
m, n = model.getDim()
X, y = model.exportedAsClassicDataset()
pI = model.partitionIndexes
X_train = X[:pI[1]]
y_train = y[:pI[1]]
X_test = X[pI[1]:]
y_test = y[pI[1]:]
eigenfaceModel = EigenfaceModel(X_train,
y_train,
nmin=4,
nmax=150,
m=m,
n=n)
epsilons = zeros(len(y_test))
for i, X_i in enumerate(X_test):
Phi_i = X_i - eigenfaceModel.mean_face
Ω_i = eigenfaceModel.projectOntoFacespace(X_i)
#Phi_f = zeros(len(Phi_i))
#Phi_f[:len(Ω_i)] = Ω_i
#epsilons[i] = norm(Phi_i - Phi_f)
epsilons[i] = sqrt(abs(Phi_i.T.dot(Phi_i) -
Ω_i.T.dot(Ω_i))) #sqrt(norm(X_i - Ω_i))
#print(argmax(epsilons))
#print(model.files[argmax(epsilons)])
# 4 possibilities (from paper)
# (1) near face space and near a face class
# (2) near face space but not near to a known face class
# (3) distant from face space and near a face class
# (4) distant from face space and not near a known face class
plotHist(epsilons, "Distance between image and face-space",
"Distance Hist for faces that should NOT be known", color)
print(max(epsilons))
return X_train, y_train, X_test, y_test, eigenfaceModel, model
print(
classification_report(y_test,
y_predict,
target_names=faces95_own_classes))
if doPlot:
plotConfusionMatrix(y_test, y_predict, labels=faces95_own_classes)
# ---------------------- OTHER ALGORITHMS -----------------------------
from sklearn.neural_network import MLPClassifier
X_train_pca = eigenfaceModel.exportTrainPca()
X_test_pca = []
for X_i in X_test:
X_test_pca.append(eigenfaceModel.projectOntoFacespace(X_i))
X_test_pca = array(X_test_pca)
# train a neural network
print("Fitting the classifier to the training set")
clf = MLPClassifier(hidden_layer_sizes=(
1024,
1024,
),
batch_size='auto',
verbose=True,
early_stopping=True).fit(X_train_pca, y_train)
print(clf)
y_pred = clf.predict(X_test_pca)
print("#--------------------------------------------------------------")
print("#- NEURAL NETWORKS -")