test_files_count = 0
valid_superpixels = []
validationOriginalImage = []
test_superpixels = []
testOriginalImage = []
train_superpixels = []
for i in xrange(0, num_files):
fe = Feature()
fe.loadImage(im_file_names[i])
fe.loadSuperpixelImage()
#fe.loadSuperpixelFromFile(sp_file_names[i])
fe.loadLabelImage(label_file_names[i])
featureVectors = fe.getFeaturesVectors()
labels = fe.getSuperPixelLabels()
#Test purposes
edges, edgeFeatures1, edgeFeatures2 = fe.getEdges()
if file_labels[i] != TESTING_LABEL:
# store data
if file_labels[i] == TRAINING_LABEL:
train_edges.append(edges)
train_edgesFeatures1.append(edgeFeatures1)
train_edgesFeatures2.append(edgeFeatures2)
train_superpixels.append(fe.getSuperpixelImage())
train_labels = np.append(train_labels, labels, 0)
if train_data == []:
train_data = featureVectors
else:
valid_superpixels = []
validationOriginalImage = []
test_superpixels = []
testOriginalImage = []
train_superpixels = []
for i in xrange(0,num_files):
fe = Feature()
fe.loadImage(im_file_names[i])
fe.loadSuperpixelImage()
#fe.loadSuperpixelFromFile(sp_file_names[i])
fe.loadLabelImage(label_file_names[i])
featureVectors = fe.getFeaturesVectors()
labels = fe.getSuperPixelLabels()
#Test purposes
edges, edgeFeatures1, edgeFeatures2 = fe.getEdges()
if file_labels[i] != TESTING_LABEL:
# store data
if file_labels[i] == TRAINING_LABEL:
train_edges.append(edges)
train_edgesFeatures1.append(edgeFeatures1)
train_edgesFeatures2.append(edgeFeatures2)
train_superpixels.append(fe.getSuperpixelImage())
train_labels = np.append(train_labels, labels, 0)
if train_data==[]:
train_data = featureVectors
else:
