在Python中寻找无特定输入点的两幅图像间的仿射变换

import cv2
import numpy as np

# 读取两幅图像
image1 = cv2.imread('image1.jpg')
image2 = cv2.imread('image2.jpg')

# 将图像转换为灰度图
gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)

# 使用SIFT算法检测关键点和特征描述子
sift = cv2.SIFT_create()
keypoints1, descriptors1 = sift.detectAndCompute(gray1, None)
keypoints2, descriptors2 = sift.detectAndCompute(gray2, None)

# 使用FLANN匹配器进行特征点匹配
matcher = cv2.FlannBasedMatcher()
matches = matcher.match(descriptors1, descriptors2)

# 选择最佳匹配的特征点
best_matches = sorted(matches, key=lambda x: x.distance)[:10]

# 提取最佳匹配的特征点的坐标
src_pts = np.float32([keypoints1[m.queryIdx].pt for m in best_matches]).reshape(-1, 1, 2)
dst_pts = np.float32([keypoints2[m.trainIdx].pt for m in best_matches]).reshape(-1, 1, 2)

# 估计仿射变换矩阵
M, _ = cv2.estimateAffine2D(src_pts, dst_pts)

# 对图像进行仿射变换
result = cv2.warpAffine(image1, M, (image2.shape[1], image2.shape[0]))

# 显示结果图像
cv2.imshow('Result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()