def preprocessor_pca_modular (data): from modshogun import RealFeatures from modshogun import PCA features = RealFeatures(data) preprocessor = PCA() preprocessor.init(features) preprocessor.apply_to_feature_matrix(features) return features
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!"
#Reconstruction with diferents values of eigenvectos
#Read the last image of the file to test Eigenfaces
image = cv2.resize(cv2.imread(list_filenames[0], cv2.IMREAD_GRAYSCALE),
(IMAGE_HEIGHT, IMAGE_WIDHT))
#image as row
imageAsRow = np.asarray(image.reshape(image.shape[0] * image.shape[1], 1),
np.float64)
#Reconstruct 10 eigen vectors to 300, step 15
for i in range(10, 300, 50):
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()
