ORB feature detector and binary descriptor

This example demonstrates the ORB feature detection and binary description algorithm. It uses an oriented FAST detection method and the rotated BRIEF descriptors.

Unlike BRIEF, ORB is comparatively scale and rotation invariant while still employing the very efficient Hamming distance metric for matching. As such, it is preferred for real-time applications.

from skimage import data
from skimage import transform
from skimage.feature import match_descriptors, ORB, plot_matches
from skimage.color import rgb2gray
import matplotlib.pyplot as plt


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 = ORB(n_keypoints=200)

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, cross_check=True)
matches13 = match_descriptors(descriptors1, descriptors3, cross_check=True)

fig, ax = plt.subplots(nrows=2, ncols=1)

plt.gray()

plot_matches(ax[0], img1, img2, keypoints1, keypoints2, matches12)
ax[0].axis('off')
ax[0].set_title("Original Image vs. Transformed Image")

plot_matches(ax[1], img1, img3, keypoints1, keypoints3, matches13)
ax[1].axis('off')
ax[1].set_title("Original Image vs. Transformed Image")


plt.show()