SIFT feature detector and descriptor extractor

This example demonstrates the SIFT feature detection and its description algorithm.

The scale-invariant feature transform (SIFT) [1] was published in 1999 and is still one of the most popular feature detectors available, as its promises to be “invariant to image scaling, translation, and rotation, and partially in-variant to illumination changes and affine or 3D projection” [2]. Its biggest drawback is its runtime, that’s said to be “at two orders of magnitude” [3] slower than ORB, which makes it unsuitable for real-time applications.

References

[1]

https://en.wikipedia.org/wiki/Scale-invariant_feature_transform

[2]

D.G. Lowe. “Object recognition from local scale-invariant features”, Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, vol.2, pp. 1150-1157. DOI:10.1109/ICCV.1999.790410

[3]

Ethan Rublee, Vincent Rabaud, Kurt Konolige and Gary Bradski “ORB: An efficient alternative to SIFT and SURF” http://www.gwylab.com/download/ORB_2012.pdf

import matplotlib.pyplot as plt

from skimage import data
from skimage import transform
from skimage.color import rgb2gray
from skimage.feature import match_descriptors, plot_matched_features, SIFT

img1 = rgb2gray(data.astronaut())
img2 = transform.rotate(img1, 180)
tform = transform.AffineTransform(scale=(1.3, 1.1), rotation=0.5, translation=(0, -200))
img3 = transform.warp(img1, tform)

descriptor_extractor = SIFT()

descriptor_extractor.detect_and_extract(img1)
keypoints1 = descriptor_extractor.keypoints
descriptors1 = descriptor_extractor.descriptors

descriptor_extractor.detect_and_extract(img2)
keypoints2 = descriptor_extractor.keypoints
descriptors2 = descriptor_extractor.descriptors

descriptor_extractor.detect_and_extract(img3)
keypoints3 = descriptor_extractor.keypoints
descriptors3 = descriptor_extractor.descriptors

matches12 = match_descriptors(
    descriptors1, descriptors2, max_ratio=0.6, cross_check=True
)
matches13 = match_descriptors(
    descriptors1, descriptors3, max_ratio=0.6, cross_check=True
)
fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(11, 8))

plt.gray()

plot_matched_features(
    img1,
    img2,
    keypoints0=keypoints1,
    keypoints1=keypoints2,
    matches=matches12,
    ax=ax[0, 0],
)
ax[0, 0].axis('off')
ax[0, 0].set_title("Original Image vs. Flipped Image\n" "(all keypoints and matches)")

plot_matched_features(
    img1,
    img3,
    keypoints0=keypoints1,
    keypoints1=keypoints3,
    matches=matches13,
    ax=ax[1, 0],
)
ax[1, 0].axis('off')
ax[1, 0].set_title(
    "Original Image vs. Transformed Image\n" "(all keypoints and matches)"
)

plot_matched_features(
    img1,
    img2,
    keypoints0=keypoints1,
    keypoints1=keypoints2,
    matches=matches12[::15],
    ax=ax[0, 1],
    only_matches=True,
)
ax[0, 1].axis('off')
ax[0, 1].set_title(
    "Original Image vs. Flipped Image\n" "(subset of matches for visibility)"
)

plot_matched_features(
    img1,
    img3,
    keypoints0=keypoints1,
    keypoints1=keypoints3,
    matches=matches13[::15],
    ax=ax[1, 1],
    only_matches=True,
)
ax[1, 1].axis('off')
ax[1, 1].set_title(
    "Original Image vs. Transformed Image\n" "(subset of matches for visibility)"
)

plt.tight_layout()
plt.show()