def train_sift(self,X_list):
bow_list = []
for X in X_list:
bow_list.append(self.compute(X))
self.bow_matrix = reduce(np.vstack,bow_list)
dh = DataHandler()
dh.load()
sample_y = np.empty((len(X_list),1))
for i in range(len(sample_y)):
sample_y[i][0] = dh.get_lables(id=i)
sample_data = np.hstack(sample_y,self.bow_matrix)
# save sample data
np.savetxt(os.path.join(self.bow_path,'bow_sift.txt'),sample_data)
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()
