kp,desc = sift.compute(gray,kp)
#print desc
if desc != None:
img_features = []
for row in desc:
i+=1
vocab.append(row.tolist())
img_features.append(row.tolist())
raw_corpus.append(img_features)
imp.append(i)
#print raw_corpus
# Perform clustering with k clusters. This will probably need tuning.
cluster = KMeans(k, n_init=1)
cluster.fit(vocab)
#print minicorpus
# Now we build the clustered corpus where each entry is a string containing the cluster ids for each sift-feature.
corpus = []
for entry in raw_corpus:
corpus.append(' '.join([str(x) for x in cluster.predict(entry)]))
#print corpus
#now we are setting our features and thereby normalizing our values
#print setfeatures.setFeatures(corpus,k)
table = normalizefraction.normalize(setfeatures.setFeatures(corpus,k),k)
#print table
with open(var.outputfile, var.option) as f:
writer = csv.writer(f)
writer.writerows(table)
kp,desc = sift.detectAndCompute(gray,None)
#print desc
if desc != None:
img_features = []
for row in desc:
#i+=1
vocab.append(row.tolist())
img_features.append(row.tolist())
raw_corpus.append(img_features)
#imp.append(i)
#print raw_corpus
# Perform clustering with k clusters. This will probably need tuning.
cluster = KMeans(k, n_init=1)
cluster.fit(vocab)
#print minicorpus
# Now we build the clustered corpus where each entry is a string containing the cluster ids for each sift-feature.
corpus = []
for entry in raw_corpus:
corpus.append(' '.join([str(x) for x in cluster.predict(entry)]))
#print corpus
#now we are setting our features and thereby normalizing our values
#print setfeatures.setFeatures(corpus,k)
table = normalize.normalize(setfeatures.setFeatures(corpus,k),k)
#print table
with open(var.outputfile, var.option) as f:
writer = csv.writer(f)
writer.writerows(table)
