BRIEF binary descriptor

This example demonstrates the BRIEF binary description algorithm. The descriptor consists of relatively few bits and can be computed using a set of intensity difference tests. The short binary descriptor results in low memory footprint and very efficient matching based on the Hamming distance metric. BRIEF does not provide rotation-invariance. Scale-invariance can be achieved by detecting and extracting features at different scales.

/home/runner/work/scikit-image/scikit-image/doc/examples/features_detection/plot_brief.py:58: FutureWarning:

`plot_matches` is deprecated since version 0.23 and will be removed in version 0.25. Use `skimage.feature.plot_matched_features` instead.

/home/runner/work/scikit-image/scikit-image/doc/examples/features_detection/plot_brief.py:62: FutureWarning:

`plot_matches` is deprecated since version 0.23 and will be removed in version 0.25. Use `skimage.feature.plot_matched_features` instead.

from skimage import data
from skimage import transform
from skimage.feature import (
    match_descriptors,
    corner_peaks,
    corner_harris,
    plot_matches,
    BRIEF,
)
from skimage.color import rgb2gray
import matplotlib.pyplot as plt


img1 = rgb2gray(data.astronaut())
tform = transform.AffineTransform(scale=(1.2, 1.2), translation=(0, -100))
img2 = transform.warp(img1, tform)
img3 = transform.rotate(img1, 25)

keypoints1 = corner_peaks(corner_harris(img1), min_distance=5, threshold_rel=0.1)
keypoints2 = corner_peaks(corner_harris(img2), min_distance=5, threshold_rel=0.1)
keypoints3 = corner_peaks(corner_harris(img3), min_distance=5, threshold_rel=0.1)

extractor = BRIEF()

extractor.extract(img1, keypoints1)
keypoints1 = keypoints1[extractor.mask]
descriptors1 = extractor.descriptors

extractor.extract(img2, keypoints2)
keypoints2 = keypoints2[extractor.mask]
descriptors2 = extractor.descriptors

extractor.extract(img3, keypoints3)
keypoints3 = keypoints3[extractor.mask]
descriptors3 = 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()