python opencv旋转图片并且配对然后返还旋转角度的代码

以下是Python OpenCV旋转图片并且配对返回旋转角度的代码：

import cv2
import numpy as np

def rotate_and_match(img1, img2):
    # Convert images to grayscale
    gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
    gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
    
    # Find keypoints and descriptors using SIFT
    sift = cv2.xfeatures2d.SIFT_create()
    kp1, des1 = sift.detectAndCompute(gray1, None)
    kp2, des2 = sift.detectAndCompute(gray2, None)

    # Match keypoints using FLANN matcher
    FLANN_INDEX_KDTREE = 0
    index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
    search_params = dict(checks=50)
    flann = cv2.FlannBasedMatcher(index_params, search_params)
    matches = flann.knnMatch(des1, des2, k=2)

    # Apply ratio test to select good matches
    good_matches = []
    for m, n in matches:
        if m.distance < 0.7 * n.distance:
            good_matches.append(m)

    # Get the angle of rotation using RANSAC algorithm
    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)
    angle = np.rad2deg(np.arctan2(M[1, 0], M[0, 0]))

    # Rotate the second image and display the matching result
    rows, cols = img2.shape[:2]
    rotated_img2 = cv2.warpPerspective(img2, M, (cols, rows))
    result = cv2.hconcat([img1, rotated_img2])
    for m in good_matches:
        pt1 = tuple(np.int32(kp1[m.queryIdx].pt))
        pt2 = tuple(np.int32(kp2[m.trainIdx].pt))
        pt2 = (pt2[0] + cols, pt2[1])
        cv2.line(result, pt1, pt2, (0, 255, 0), 1)

    # Show the result and return the angle of rotation
    cv2.imshow('Matching Result', result)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    return angle

如果图像缺少SIFT功能，则可以使用ORB（用于Oriented FAST and Rotated BRIEF）代替SIFT。只需要更改以下两行代码：

sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(gray1, None)
kp2, des2 = sift.detectAndCompute(gray2, None)

替换成:

orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(gray1, None)
kp2, des2 = orb.detectAndCompute(gray2, None)

对于更好的性能和精度，可以使用其他更先进的算法，如SURF，但需要额外的库支持（请参见OpenCV文档以获取更多信息）。