atep_bovw = kernels.compute_ATEP_kernels(feats_path + '/bovwtree/', videonames, traintest_parts, xml_config['features_list'], \ kernels_path + '/atep-bovw/', kernel_type='intersection', norm='l1', power_norm=False, \ use_disk=False, nt=args.nt, verbose=args.verbose) params = [[[1]], [1], np.linspace(0,1,21), desc_weights_gbl] results = classification.classify(atep_bovw, \ class_labels, traintest_parts, params, \ xml_config['features_list'], \ C=C_gbl, strategy=strategy_gbl, opt_criterion=opt_criterion, verbose=args.verbose) classification.print_results(results) if 'atep-fv' in args.methods: tracklet_representation.train_fv_gmms(tracklets_path, videonames, traintest_parts, xml_config['features_list'], intermediates_path, pca_reduction=True, nt=args.nt, verbose=args.verbose) tracklet_representation.compute_fv_descriptors_multithread(tracklets_path, intermediates_path, videonames, traintest_parts, xml_config['features_list'], \ feats_path + '/fvtree/', \ treelike=True, pca_reduction=True, clusters_path=clusters_path, nt=args.nt, verbose=args.verbose) atep_fv = kernels.compute_ATEP_kernels(feats_path + '/fvtree/', videonames, traintest_parts, xml_config['features_list'], \ kernels_path + '/atep-fv/', use_disk=False, nt=args.nt, verbose=args.verbose) params = [[[1]], [1], np.linspace(0,1,21), desc_weights_gbl] results = classification.classify(atep_fv, \ class_labels, traintest_parts, params, \ xml_config['features_list'], \ C=C_gbl, strategy=strategy_gbl, opt_criterion=opt_criterion, verbose=args.verbose)
params = [[[1]], [1], np.linspace(0, 1, 21), desc_weights_gbl] results = classification.classify(atep_bovw, \ class_labels, traintest_parts, params, \ xml_config['features_list'], \ C=C_gbl, strategy=strategy_gbl, opt_criterion=opt_criterion, verbose=args.verbose) classification.print_results(results) if 'atep-fv' in args.methods: tracklet_representation.train_fv_gmms(tracklets_path, videonames, traintest_parts, xml_config['features_list'], intermediates_path, pca_reduction=True, nt=args.nt, verbose=args.verbose) tracklet_representation.compute_fv_descriptors_multithread(tracklets_path, intermediates_path, videonames, traintest_parts, xml_config['features_list'], \ feats_path + '/fvtree/', \ treelike=True, pca_reduction=True, clusters_path=clusters_path, nt=args.nt, verbose=args.verbose) atep_fv = kernels.compute_ATEP_kernels(feats_path + '/fvtree/', videonames, traintest_parts, xml_config['features_list'], \ kernels_path + '/atep-fv/', use_disk=False, nt=args.nt, verbose=args.verbose) params = [[[1]], [1], np.linspace(0, 1, 21), desc_weights_gbl] results = classification.classify(atep_fv, \ class_labels, traintest_parts, params, \ xml_config['features_list'], \ C=C_gbl,
tracklet_clustering.cluster(tracklets_path, videonames, INSTANCE_ST, INSTANCE_TOTAL, clusters_path, visualize=False) tracklet_representation.train_bovw_codebooks( tracklets_path, videonames, traintest_parts, INTERNAL_PARAMETERS['feature_types'], intermediates_path, pca_reduction=False) tracklet_representation.train_fv_gmms(tracklets_path, videonames, traintest_parts, INTERNAL_PARAMETERS['feature_types'], intermediates_path) tracklet_representation.compute_bovw_descriptors(tracklets_path, intermediates_path, videonames, traintest_parts, \ INSTANCE_ST, INSTANCE_TOTAL, \ INTERNAL_PARAMETERS['feature_types'], feats_path + 'bovwtree/', \ pca_reduction=False, treelike=True, global_repr=True, clusters_path=clusters_path) tracklet_representation.compute_fv_descriptors(tracklets_path, intermediates_path, videonames, traintest_parts, \ INSTANCE_ST, INSTANCE_TOTAL, \ INTERNAL_PARAMETERS['feature_types'], feats_path + 'fvtree/', \ treelike=True, global_repr=True, clusters_path=clusters_path) c = [ 0.001, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100, 500, 1000, 5000, 10000 ]
elif len(sys.argv) < 4: INSTANCE_ST = int(sys.argv[1]) if INSTANCE_ST > len(videonames) - 1: INSTANCE_ST = len(videonames) - 1 INSTANCE_TOTAL = int(sys.argv[2]) if INSTANCE_ST + int(sys.argv[2]) > len(videonames): INSTANCE_TOTAL = len(videonames) - INSTANCE_ST print('INSTANCE_ST: %d, INSTANCE_TOTAL: %d' % (INSTANCE_ST, INSTANCE_TOTAL)) tracklet_extraction.extract(fullvideonames, videonames, INSTANCE_ST, INSTANCE_TOTAL, INTERNAL_PARAMETERS['feature_types'], tracklets_path) tracklet_clustering.cluster(tracklets_path, videonames, INSTANCE_ST, INSTANCE_TOTAL, clusters_path, visualize=False) tracklet_representation.train_bovw_codebooks(tracklets_path, videonames, traintest_parts, INTERNAL_PARAMETERS['feature_types'], intermediates_path, pca_reduction=False) tracklet_representation.train_fv_gmms(tracklets_path, videonames, traintest_parts, INTERNAL_PARAMETERS['feature_types'], intermediates_path) tracklet_representation.compute_bovw_descriptors(tracklets_path, intermediates_path, videonames, traintest_parts, \ INSTANCE_ST, INSTANCE_TOTAL, \ INTERNAL_PARAMETERS['feature_types'], feats_path + 'bovwtree/', \ pca_reduction=False, treelike=True, global_repr=True, clusters_path=clusters_path) tracklet_representation.compute_fv_descriptors(tracklets_path, intermediates_path, videonames, traintest_parts, \ INSTANCE_ST, INSTANCE_TOTAL, \ INTERNAL_PARAMETERS['feature_types'], feats_path + 'fvtree/', \ treelike=True, global_repr=True, clusters_path=clusters_path) c = [0.001, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100, 500, 1000, 5000, 10000] st_time = time.time() results = classification.classify(feats_path + 'bovwtree/', videonames, class_labels, traintest_parts, \ np.linspace(0, 1, 11), INTERNAL_PARAMETERS['feature_types'], \