elif args.descriptor == 'bow_dense_sift':
sift_descriptors = SIFT(nfeatures=300, type='DENSE', dense_sift_step=6)
bow_descriptor = BOW(k=512, pyramid_levels=pyramid_levels)
else:
sift_descriptors = None
bow_descriptor = None
print('Invalid descriptor')
myEvaluation = Evaluation(evaluation_path=args.evaluation_path,
save_plots=True)
if args.do_train:
start_time = time.time()
train_data = InputData.get_labeled_data()
if args.do_compute_features:
#descriptors, descriptors_idx = sift_descriptors.extract_features_simple(data_dictionary=train_data)
train_descriptors_path = "/imatge/mgorriz/work/master/models/session02/test2/train_descriptors.npy"
descriptors, descriptors_idx = \
sift_descriptors.extract_features_simple(data_dictionary=train_data,
load=True, path=train_descriptors_path)
codebook = bow_descriptor.compute_codebook(descriptors)
bow_descriptor.save(
codebook, os.path.join(args.codebook_path, 'codebook.pkl'),
'codebook')
train_visual_words = bow_descriptor.extract_features(
descriptors,
