def kMean(clusterNum):
g = Graph(r"E:\ds2018")
vocaDir = r"temp/vocabulary/"
if not os.path.exists(vocaDir):
os.makedirs(vocaDir)
sift = SIFT()
centers = []
for i in range(g.getTypeNum()):
print("[kmeans]:" + str(i))
imgPaths = g.getTrainSet(i)
features = np.float32([]).reshape((0, 128))
for imgPath, type in imgPaths:
imgMat = g.getGreyGraph(imgPath)
if imgMat is None:
print("[kmean]:" + imgPath + " is None")
continue
feature = sift.getFeature(imgMat)
features = np.append(features, feature, axis=0)
kmeans = KMeans(n_clusters=clusterNum).fit(features)
filename = os.path.join(vocaDir, str(i) + ".npy")
np.save(filename, kmeans.cluster_centers_)
centers.append(kmeans.cluster_centers_)
return centers
def loadVoca():
g = Graph(r"E:\dataset")
vocaDir = r"temp/vocabulary/"
centers = []
for i in range(g.getTypeNum()):
filePath = os.path.join(vocaDir, str(i) + '.npy')
_, center = np.load(filePath)
centers.append(centers)
return centers