valid_pixels_labels = []
valid_superpixels = []
for i in xrange(0,num_images):
feature = Feature()
feature.create(image_filenames[i],label_image_filenames[i])
labels = feature.getSuperpixelLabels()
feature_vectors = feature.getFeaturesVectors()
h, img_filename = os.path.split(image_filenames[i])
h, label_img_filename = os.path.split(label_image_filenames[i])
folder = "extracted/" + img_filename + "-" + label_img_filename + "/"
if not os.path.exists(folder):
os.makedirs(folder)
scipy.io.savemat(folder+"superpixel", {'features':feature.getSuperpixelImage()}, oned_as='column')
scipy.io.savemat(folder+"superpixel_labels", {'features':feature.getSuperpixelLabels()}, oned_as='column')
scipy.io.savemat(folder+"location", {'features':feature.getSuperpixelsLocation()}, oned_as='column')
scipy.io.savemat(folder+"color", {'features':feature.getSuperpixelsColor()}, oned_as='column')
scipy.io.savemat(folder+"hog", {'features':feature.getSuperpixelsHog()}, oned_as='column')
scipy.io.savemat(folder+"size", {'features':feature.getSuperpixelsSize()}, oned_as='column')
scipy.io.savemat(folder+"texture", {'features':feature.getSuperpixelsTexture()}, oned_as='column')
scipy.io.savemat(folder+"features_combined", {'features':feature.getFeaturesVectors()}, oned_as='column')
if file_labels[i] != TESTING:
# store data
if file_labels[i] == TRAINING:
train_superpixels.append(feature.getSuperpixelImage())
train_labels = np.append(train_labels, labels, 0)
if train_data==[]:
train_data = feature_vectors
