def predict(image_path, sess, sr):
print image_path
seg = Segmentation(image_path)
d = seg.get_labels()
mst = MinimumSpanningTree(d).get_mst()
pa = Partition(mst, seg, sess, sr)
l = pa.getList()
c = Classify()
result = c.classify(l)
img_prediction = ImgPred(basename(image_path), l, result[1])
return img_prediction
def predict(image_path, sess, sr):
"""
Add your code here
"""
"""
# Don't forget to store your prediction into ImgPred
img_prediction = ImgPred(...)
"""
print image_path
seg = Segmentation(image_path)
d = seg.get_labels()
mst = MinimumSpanningTree(d).get_mst()
pa = Partition(mst, seg, sess, sr)
l = pa.getList()
c = Classify()
result = c.classify(l)
img_prediction = ImgPred(basename(image_path), l, result[1])
return img_prediction
