vlad.py 文件源码

def cal_vlad(self, descriptors, centers):
        if self.flag is False:
            self.load()
        if self.stdSlr is not None:
            descriptors = self.stdSlr.transform(descriptors)
        dimensions = descriptors[0].shape[0]
        vlad_vector = np.zeros((len(centers),dimensions), dtype=np.float32)
        center_idx, distance = vq(descriptors, centers)
        for i,idx in enumerate(center_idx):
            vlad_vector[idx] += (descriptors[i] - centers[idx])
        vlad_vector = cv2.normalize(vlad_vector)
        vlad_vector = vlad_vector.flatten()
        return vlad_vector