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 getFeatVecForSvm(self, imgList, load=0):
if load == 1:
feats = np.load(r"temp/featVectHog.npy")
return feats
g = Graph(r"E:\ds2018")
feats = np.float32([]).reshape((0, self.getVecLength()))
for imgPath, type in imgList:
mat = g.getGreyGraph(imgPath)
if mat is None:
continue
feat = self.getFeature(mat)
feats = np.append(feats, feat, axis=0)
np.save(r"temp/featVectHog.npy", feats)
return feats
def calcVectorForSvm(self, imgList, n_cluster, load=0):
if load == 1:
types = np.load(r"temp/types.npy")
featVec = np.load(r"temp/featVectSift.npy")
centers = np.load(r"temp/centersSift.npy")
return (types, featVec, centers)
g = Graph(r"E:\ds2018")
featMat = np.float32([]).reshape((0, 128))
featList = []
featVec = np.float32([]).reshape((0, n_cluster))
types = np.float32([])
i = 0
for imgPath, type in imgList:
print("[kmeans before]:" + str(i))
i += 1
mat = g.getGreyGraph(imgPath)
if mat is None:
continue
feat = self.getFeature(mat)
featList.append(feat)
featMat = np.append(featMat, feat, axis=0)
types = np.append(types, np.float32([type]))
kmeans = KMeans(n_cluster)
kmeans.fit(featMat)
centers = kmeans.cluster_centers_
i = 0
for feature in featList:
print("[kmeans after]:" + str(i))
i += 1
featVec = np.append(featVec,
self._calcFeatVec(feature, kmeans, n_cluster,
centers),
axis=0)
np.save(r"temp/types.npy", types)
np.save(r"temp/featVectSift.npy", featVec)
np.save(r"temp/centersSift.npy", centers)
return (types, featVec, centers)
def getFeatVecForSvm(self, imgList, load=0):
if load == 1:
feats = np.load(r"temp/featVectLbp.npy")
return feats
g = Graph(r"E:\ds2018")
feats = np.float32([]).reshape((0, self.getVecLength()))
i = 0
for imgPath, type in imgList:
print("[lbp]:" + str(i))
i += 1
mat = g.getGreyGraph(imgPath)
if mat is None:
continue
feat = self.getFeature(mat)
feats = np.append(feats, feat.reshape((1, -1)), axis=0)
np.save(r"temp/featVectLbp.npy", feats)
return feats
from sklearn import svm
from Graph.Graph import Graph
from Features.LBP import LBP
from sklearn.decomposition import PCA
import numpy as np
g = Graph(r"E:\ds2018")
if not g.isDivided():
g.divideTrainTest("ds2018")
trainList = g.readTrainCSV()
features = np.float32([]).reshape(0, 128 * 128)
types = []
for imgPath, type in trainList:
lbp = LBP([8], [1], "default")
imgMatrix = g.getGreyGraph(imgPath)
if imgMatrix is None:
continue
lbpFeatures = lbp.getFeature(imgMatrix)
for j in lbpFeatures:
print(features.shape)
print(j.shape)
np.append(features, j.reshape(1, 128 * 128), axis=0)
types.append(type)
clf = svm.SVC(C=0.8, kernel='rbf', gamma=20, decision_function_shape='ovr')
clf.fit(features, types)
print("done")
'''
def __init__(self,ps,rs,method="default"):
self.p=ps
self.r=rs
self.method=method
def getFeature(self,imgMat):
features=[]
for i in self.p:
for j in self.r:
features.append(feature.local_binary_pattern(imgMat,i,j,method=self.method))
return features
g=Graph(r"E:\ds2018")
trainList=g.readTrainCSV()
lbp=LBP([8],[1])
features=[]
print(trainList)
for img in trainList:
#print(img[0])
mat=g.getGreyGraph(img[0])
if mat is None:
print("None")
continue
features.extend(lbp.getFeature(mat))
