利用Python实现对两张图片按特征点重新拼接Python代码

以下是对两张图片按特征点重新拼接的Python代码示例，假设两张图片分别为img1.png和img2.png：

import cv2
import numpy as np

# 读取图片
img1 = cv2.imread('img1.png')
img2 = cv2.imread('img2.png')

# 提取特征点和特征描述子
sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

# 匹配特征点
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)

# 筛选匹配点对
good_matches = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good_matches.append(m)

# 计算单应性变换矩阵
src_pts = np.float32([kp1[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)
dst_pts = np.float32([kp2[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)

# 变换第一张图片的位置
h, w = img1.shape[:2]
transformed_img = cv2.warpPerspective(img1, M, (w, h))

# 拼接两张图片
result = np.zeros((max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1], 3), dtype=np.uint8)
result[:img1.shape[0], :img1.shape[1]] = transformed_img
result[:img2.shape[0], img1.shape[1]:] = img2

# 显示拼接结果
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

需要注意的是，以上代码仅为示例，实际应用中可能需要根据具体情况进行一些调整和优化。