ORB关键点匹配

from skimage import transform as transform
from skimage.feature import (match_descriptors, ORB, plot_matches)
img1=rgb2gray(imread('C:/Users/xpp/Desktop/Lena.png'))#将彩色图片转换为灰度图片
img2=transform.rotate(img1,180)#图像旋转
affine_trans=transform.AffineTransform(scale=(1.3,1.1),rotation=0.5,translation=(0,-200))#图像仿射
img3=transform.warp(img1,affine_trans)
img4=transform.resize(rgb2gray(imread('C:/Users/xpp/Desktop/face.png')),img1.shape,anti_aliasing=True)
descriptor_extractor=ORB(n_keypoints=200)#ORB关键点匹配
descriptor_extractor.detect_and_extract(img1)
keypoints1,descriptors1=descriptor_extractor.keypoints,descriptor_extractor.descriptors#特征点检测
descriptor_extractor.detect_and_extract(img2)
keypoints2,descriptors2=descriptor_extractor.keypoints,descriptor_extractor.descriptors#特征点检测
descriptor_extractor.detect_and_extract(img3)
keypoints3,descriptors3=descriptor_extractor.keypoints,descriptor_extractor.descriptors#特征点检测
descriptor_extractor.detect_and_extract(img4)
keypoints4,descriptors4=descriptor_extractor.keypoints,descriptor_extractor.descriptors#特征点检测
matches12=match_descriptors(descriptors1,descriptors2,cross_check=True)#图像匹配#特征点检测
matches13=match_descriptors(descriptors1,descriptors3,cross_check=True)#图像匹配
matches14=match_descriptors(descriptors1,descriptors4,cross_check=True)#图像匹配
fig, axes=pylab.subplots(nrows=3,ncols=1,figsize=(20,25))
pylab.gray()
plot_matches(axes[0],img1,img2,keypoints1,keypoints2,matches12)
axes[0].axis('off'),axes[0].set_title("Original Image vs. Transformed Image",size=20)
plot_matches(axes[1],img1,img3,keypoints1,keypoints3,matches13)
axes[1].axis('off'),axes[1].set_title("Original Image vs. Transformed Image",size=20)
plot_matches(axes[2],img1,img4,keypoints1,keypoints4,matches14)
axes[2].axis('off'),axes[2].set_title("Image1 vs. Image2",size=20)
pylab.show()