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ORB feature detector and binary descriptorΒΆ

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 as tf
from skimage.feature import (match_descriptors, corner_harris,
                             corner_peaks, ORB, plot_matches)
from skimage.color import rgb2gray
import matplotlib.pyplot as plt

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

descriptor_extractor = ORB(n_keypoints=200)

keypoints1 = descriptor_extractor.keypoints
descriptors1 = descriptor_extractor.descriptors

keypoints2 = descriptor_extractor.keypoints
descriptors2 = descriptor_extractor.descriptors

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)


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

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

Total running time of the script: ( 0 minutes 0.961 seconds)

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