INNER CODE UNIT · Python

extract

introlab/rtabmap · corelib/src/python/rtabmap_netvlad.py:53

def extract(image):
    print(f"NetVLAD python extract{image.shape}")
    global image_batch
    global net_out
    global sess
    global dim

    if(image.shape[2] == 1):
        image = np.dstack((image, image, image))

    batch = np.expand_dims(image, axis=0)
    result = sess.run(net_out, feed_dict={image_batch: batch})
    
    # All that needs to be done (only valid for NetVLAD+whitening networks!)
    # to reduce the dimensionality of the NetVLAD representation below 4096 to D
    # is to keep the first D dimensions and L2-normalize.
    if(result.shape[1] > dim):
        v = result[:, :dim]

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