import cv2
def 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 np
def 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()
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