py实现surf特征提取

import cv2def main():# 加载图像image1 = cv2.imread('image1.jpg', cv2.IMREAD_GRAYSCALE)image2 = cv2.imread('image2.jpg', cv2.IMREAD_GRAYSCALE)# 创建SURF对象surf = cv2.xfeatures2d.SURF_create()# 检测特征点和描述符keypoints1, descriptors1 = surf.detectAndCompute(image1, None)keypoints2, descriptors2 = surf.detectAndCompute(image2, None)# 绘制特征点result_image1 = cv2.drawKeypoints(image1, keypoints1, None, (0, 255, 0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)result_image2 = cv2.drawKeypoints(image2, keypoints2, None, (0, 255, 0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)# 显示图像cv2.imshow("Image 1", result_image1)cv2.imshow("Image 2", result_image2)cv2.waitKey(0)cv2.destroyAllWindows()if __name__ == "__main__":main()

import cv2
import numpy as npdef main():# 加载图像image1 = cv2.imread('image1.jpg', cv2.IMREAD_GRAYSCALE)image2 = cv2.imread('image2.jpg', cv2.IMREAD_GRAYSCALE)# 创建SURF对象surf = cv2.xfeatures2d.SURF_create()# 检测特征点和描述符keypoints1, descriptors1 = surf.detectAndCompute(image1, None)keypoints2, descriptors2 = surf.detectAndCompute(image2, None)# 创建匹配器matcher = cv2.DescriptorMatcher_create(cv2.DescriptorMatcher_FLANNBASED)matches = matcher.match(descriptors1, descriptors2)# 根据距离排序匹配项matches = sorted(matches, key=lambda x: x.distance)# 提取前10个最佳匹配项good_matches = matches[:10]# 绘制匹配点和线条result_image = cv2.drawMatches(image1, keypoints1, image2, keypoints2, good_matches, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)# 显示图像cv2.imshow("Matches", result_image)cv2.waitKey(0)cv2.destroyAllWindows()if __name__ == "__main__":main()