SIFT 特征检测器和描述子提取器

此示例演示了SIFT特征检测及其描述算法。

尺度不变特征变换（SIFT）[1]_ 于1999年发表，至今仍是可用的最受欢迎的特征检测器之一，因为它承诺“对图像缩放、平移和旋转不变，对光照变化和仿射或3D投影部分不变”[2]。其最大的缺点是运行时间，据说比ORB慢“两个数量级”[3]，这使得它不适合实时应用。

参考文献

[1]

https://en.wikipedia.org/wiki/尺度不变特征变换

[2]

D.G. Lowe. “基于局部尺度不变特征的对象识别”, 第七届IEEE国际计算机视觉会议论文集, 1999年, 第2卷, 第1150-1157页. DOI:10.1109/ICCV.1999.790410

[3]

Ethan Rublee, Vincent Rabaud, Kurt Konolige 和 Gary Bradski 的 “ORB: SIFT 和 SURF 的高效替代方案” http://www.gwylab.com/download/ORB_2012.pdf

/Users/cw/baidu/code/fin_tool/github/scikit-image/doc/examples/features_detection/plot_sift.py:67: FutureWarning:

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

/Users/cw/baidu/code/fin_tool/github/scikit-image/doc/examples/features_detection/plot_sift.py:71: FutureWarning:

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

/Users/cw/baidu/code/fin_tool/github/scikit-image/doc/examples/features_detection/plot_sift.py:77: FutureWarning:

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

/Users/cw/baidu/code/fin_tool/github/scikit-image/doc/examples/features_detection/plot_sift.py:85: FutureWarning:

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

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

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

plot_matches(
    ax[0, 1], img1, img2, keypoints1, keypoints2, matches12[::15], 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_matches(
    ax[1, 1], img1, img3, keypoints1, keypoints3, matches13[::15], 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()