Speeded-Up Robust Features¶

New in version 0.8: In version 0.8, some of the inner functions are now in mahotas.features.surf instead of mahotas.surf

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

Detection of interest points.
Description of interest points.

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

import numpy as np
from mahotas.features import surf

f = ... # input image
spoints = surf.surf(f)
print("Nr points: {}".format(len(spoints)))

Given the results, we can perform a simple clustering using, for example, ` scikit-learn:

try:
    from sklearn.cluster import KMeans

    # 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 = KMeans(n_clusters=k).fit(descrs).labels_
    colors = np.array([(255-52*i,25+52*i,37**i % 101) for i in range(k)])
except:
    values = np.zeros(100)
    colors = [(255,0,0)]

So we are assigning different colours to each of the possible

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

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

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

from __future__ import print_function
import numpy as np
import mahotas as mh
from mahotas.features import surf
from matplotlib import pyplot as plt

f = mh.demos.load('luispedro', as_grey=True)
f = f.astype(np.uint8)
spoints = surf.surf(f, 4, 6, 2)
print("Nr points:", len(spoints))

try:
    from sklearn.cluster import KMeans
    descrs = spoints[:,5:]
    k = 5
    values = KMeans(n_clusters=k).fit(descrs).labels_
    colors = np.array([(255-52*i,25+52*i,37**i % 101) for i in range(k)])
except:
    values = np.zeros(100, int)
    colors = np.array([(255,0,0)])

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

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

API Documentation¶

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

mahotas.features.surf.dense(f, spacing, scale={np.sqrt(spacing)}, is_integral=False, include_interest_point=False)

desc_array = dense(f, spacing, scale={np.sqrt(spacing)}, is_integral=False, include_interest_point=False)

Parameters:

fimage: original image
spacinginteger: Distance between points
scalefloat, optional: Scale of interest points. By default, it is set to np.sqrt(spacing)
is_integralboolean, optional: Whether f is an integral image
include_interest_pointbool, optional: Whether to return interest point information. Default is False

Returns:

descriptorsndarray: Descriptors at dense points. Note that the interest point is not returned by default.