ORB 特徵偵測器與二元描述子

此範例示範 ORB 特徵偵測和二元描述演算法。它使用定向 FAST 偵測方法和旋轉的 BRIEF 描述子。

與 BRIEF 不同，ORB 具有相對的縮放和旋轉不變性，同時仍然使用非常有效的漢明距離度量進行匹配。因此，它更適用於即時應用。

from skimage import data
from skimage import transform
from skimage.feature import match_descriptors, ORB, plot_matched_features
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_matched_features(
    img1,
    img2,
    keypoints0=keypoints1,
    keypoints1=keypoints2,
    matches=matches12,
    ax=ax[0],
)
ax[0].axis('off')
ax[0].set_title("Original Image vs. Transformed Image")

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


plt.show()