parser.add_argument('-i',action='store',dest='id_upper',type = int) parser.add_argument('-c',action='store_true',help='train classifier') parser.add_argument('-f',action='store',dest='file',type=str,help='parser a clothes image') args = parser.parse_args() if args.cmd == 'train': if args.b: if args.s: train_bow_sift(args.id_upper) elif args.p: train_bow_pixel() if args.c: train_clf('pixel') if args.cmd == 'test': kmeans = KmeansModel() kmeans.load('kmeans_pixel') clf = RandomForest() clf.load() data = DataHandler() data.load() if args.file: for res in clf.predict(kmeans,file): print int(res),data.tell_label(int(res)) if args.cmd == 'data': data = DataHandler() data.parse_data('design.json') data.save()
def test_something(self): dh = DataHandler() dh.parse_data("design.json")