class EigenFaces():
def __init__(self, num_components):
"""
Constructor
"""
self._num_components = num_components
self._projections = []
def train(self, images, labels):
"""
Train eigenfaces
"""
print "Train..."
#copy labels
self._labels = labels
#transform the numpe vector to shogun structure
features = RealFeatures(images)
#PCA
self.pca = PCA()
#set dimension
self.pca.set_target_dim(self._num_components)
#compute PCA
self.pca.init(features)
for sampleIdx in range(features.get_num_vectors()):
v = features.get_feature_vector(sampleIdx)
p = self.pca.apply_to_feature_vector(v)
self._projections.insert(sampleIdx, p)
print "Train ok!"
def predict(self, image):
"""
Predict the face
"""
#image as row
imageAsRow = np.asarray(
image.reshape(image.shape[0] * image.shape[1], 1), np.float64)
#project inthe subspace
p = self.pca.apply_to_feature_vector(
RealFeatures(imageAsRow).get_feature_vector(0))
#min value to find the face
minDist = 1e100
#class
minClass = -1
#search which face is the best match
for sampleIdx in range(len(self._projections)):
test = RealFeatures(np.asmatrix(p, np.float64).T)
projection = RealFeatures(
np.asmatrix(self._projections[sampleIdx], np.float64).T)
dist = EuclideanDistance(test, projection).distance(0, 0)
if (dist < minDist):
minDist = dist
minClass = self._labels[sampleIdx]
return minClass
def getMean(self):
"""
Return the mean vector
"""
return self.pca.get_mean()
def getEigenValues(self):
"""
Return the eigenvalues vector
"""
return self.pca.get_eigenvalues()
class EigenFaces():
def __init__(self, num_components):
"""
Constructor
"""
self._num_components = num_components;
self._projections = []
def train(self, images, labels):
"""
Train eigenfaces
"""
print "Train...",
#copy labels
self._labels = labels;
#transform the numpe vector to shogun structure
features = RealFeatures(images)
#PCA
self.pca = PCA()
#set dimension
self.pca.set_target_dim(self._num_components);
#compute PCA
self.pca.init(features)
for sampleIdx in range(features.get_num_vectors()):
v = features.get_feature_vector(sampleIdx);
p = self.pca.apply_to_feature_vector(v);
self._projections.insert(sampleIdx, p);
print "ok!"
def predict(self, image):
"""
Predict the face
"""
#image as row
imageAsRow = np.asarray(image.reshape(image.shape[0]*image.shape[1],1),
np.float64);
#project inthe subspace
p = self.pca.apply_to_feature_vector(RealFeatures(imageAsRow).get_feature_vector(0));
#min value to find the face
minDist =1e100;
#class
minClass = -1;
#search which face is the best match
for sampleIdx in range(len(self._projections)):
test = RealFeatures(np.asmatrix(p,np.float64).T)
projection = RealFeatures(np.asmatrix(self._projections[sampleIdx],
np.float64).T)
dist = EuclideanDistance( test, projection).distance(0,0)
if(dist < minDist ):
minDist = dist;
minClass = self._labels[sampleIdx];
return minClass
def getMean(self):
"""
Return the mean vector
"""
return self.pca.get_mean()
def getEigenValues(self):
"""
Return the eigenvalues vector
"""
return self.pca.get_eigenvalues();
pca = PCA()
#set dimension
pca.set_target_dim(i)
#compute PCA
pca.init(RealFeatures(images))
pca.apply_to_feature_vector(
RealFeatures(imageAsRow).get_feature_vector(0))
#reconstruct
projection = pca.apply_to_feature_vector(
RealFeatures(imageAsRow).get_feature_vector(0))
reconstruction = np.asmatrix( np.asarray(projection, np.float64))* \
np.asmatrix( pca.get_transformation_matrix()).T
reconstruction = reconstruction + pca.get_mean()
#normlize image
reconstruction_normalize = np.zeros((IMAGE_HEIGHT, IMAGE_WIDHT, 1),
np.uint8)
reconstruction_normalize = cv2.normalize(reconstruction,
reconstruction_normalize, 0,
255, cv2.NORM_MINMAX,
cv2.CV_8UC1)
reconstruction_normalize = reconstruction_normalize.reshape(
IMAGE_HEIGHT, IMAGE_WIDHT)
#show reconstruction
cv2.imshow("reconstruction" + str(i), reconstruction_normalize)
cv2.waitKey(0)
#Reconstruct 10 eigen vectors to 300, step 15
for i in reconstructions:
print "Reconstruct with " + str(i) + " eigenvectors"
pca = PCA()
#set dimension
pca.set_target_dim(i);
#compute PCA
pca.init(RealFeatures(images))
pca.apply_to_feature_vector(RealFeatures(imageAsRow)
.get_feature_vector(0));
#reconstruct
projection = pca.apply_to_feature_vector(RealFeatures(imageAsRow)
.get_feature_vector(0));
reconstruction = np.asmatrix( np.asarray(projection, np.float64))* \
np.asmatrix( pca.get_transformation_matrix()).T
reconstruction = reconstruction + pca.get_mean()
#prepare the data to visualize in one window
show_images_titles.append( str(i) + " eigenvectors" );
show_images.append(reconstruction.reshape(IMAGE_HEIGHT, IMAGE_WIDHT))
plot_gallery(show_images, show_images_titles, IMAGE_HEIGHT,
IMAGE_WIDHT, 4, 4);
pl.show()
