Speeded-Up Robust Features

New in version 0.6.1: SURF is only available starting in version 0.6.1, with an important bugfix in version 0.6.2.

Speeded-Up Robust Features (SURF) are a recent innnovation in the local features family. There are two steps to this algorithm:

1. Detection of interest points.
2. Description of interest points.

The function mahotas.surf.surf combines the two steps:

import numpy as np
import mahotas.surf

f = ... # input image
spoints = mahotas.surf.surf(f)
print "Nr points:", len(spoints)

Given the results, we can perform a simple clustering using, for example, milk (we could have used any other system, of course; having written milk, I am most familiar with it):

try:
    import milk

    # spoints includes both the detection information (such as the position
    # and the scale) as well as the descriptor (i.e., what the area around
    # the point looks like). We only want to use the descriptor for
    # clustering. The descriptor starts at position 5:
    descrs = spoints[:,5:]

    # We use 5 colours just because if it was much larger, then the colours
    # would look too similar in the output.
    k = 5
    values, _  = milk.kmeans(descrs, k)
    colors = np.array([(255-52*i,25+52*i,37**i % 101) for i in xrange(k)])
except:
    values = np.zeros(100)
    colors = [(255,0,0)]

So we are assigning different colours to each of the possible

The helper mahotas.surf.show_surf draws coloured polygons around the interest points:

f2 = mahotas.surf.show_surf(f, spoints[:100], values, colors)
imshow(f2)
show()

Running the above on a photo of luispedro, the author of mahotas yields:

import numpy as np
import mahotas
from mahotas.features import surf
from pylab import *

from os import path

luispedro_image = path.join(
                    path.dirname(path.abspath(__file__)),
                    'data',
                    'luispedro.jpg')

f = mahotas.imread(luispedro_image, as_grey=True)
f = f.astype(np.uint8)
spoints = surf.surf(f, 4, 6, 2)
print "Nr points:", len(spoints)

try:
    import milk
    descrs = spoints[:,5:]
    k = 5
    values, _  =milk.kmeans(descrs, k)
    colors = np.array([(255-52*i,25+52*i,37**i % 101) for i in xrange(k)])
except:
    values = np.zeros(100)
    colors = np.array([(255,0,0)])

f2 = surf.show_surf(f, spoints[:100], values, colors)
imshow(f2)
show()

(Source code, png, hires.png, pdf)

API Documentation

The mahotas.surf module contains separate functions for all the steps in the SURF pipeline.