def train_bow_pixel():
kmeans_sift = KmeansModel()
sift = Sift()
if not kmeans_sift.load('kmeans_sift'):
print 'kmeans_sift not found!'
if not sift.load():
print 'no sift data found!'
exit(1)
def test_something(self):
id_upper = 3900
sift = Sift()
kmeans = KmeansModel()
image_list = sorted([x for x in os.listdir(DATAPATH) if os.path.splitext(x)[1] == '.jpg' and int(os.path.splitext(x)[0]) < id_upper],key=lambda x: int(os.path.splitext(x)[0]))
print image_list
descriptors_list = sift.compute(image_list)
kmeans.fit(descriptors_list)
kmeans.save('kmeans_sift')
print 'ok'
def train_bow_sift(id_upper):
if not id_upper:
id_upper = 3900
sift = Sift()
kmeans = KmeansModel()
image_list = sorted([x for x in os.listdir(DATAPATH) if os.path.splitext(x)[1] == '.jpg' and int(os.path.splitext(x)[0]) < id_upper],key=lambda x: int(os.path.splitext(x)[0]))
sift.compute(image_list)
if not kmeans.load('kmeans_sift'):
kmeans.fit(sift.descriptors_list)
kmeans.save('kmeans_sift')
bow = Bow(kmeans)
# bag of words of samples
# label indicator1 indicator2 ...
# ... ... ... ...
bow.train_sift(sift.descriptors_list)
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()
