在不知道相机内参的情况下，利用opencv库进行基于特征点匹配的极线校正

def rectify_image(img1, img2):
    # 初始化SIFT检测器
    sift = cv2.SIFT_create()
    # 检测关键点和计算描述符
    kp1, des1 = sift.detectAndCompute(img1, None)
    kp2, des2 = sift.detectAndCompute(img2, None)
    # 使用FLANN匹配器进行特征点匹配
    FLANN_INDEX_KDTREE = 1
    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)
    # 选择良好的匹配点
    src_pts = []
    dst_pts = []
    # ratio test as per Lowe's paper
    for i, (m, n) in enumerate(matches):
        if m.distance < 0.8 * n.distance:
            dst_pts.append(kp2[m.trainIdx].pt)
            src_pts.append(kp1[m.queryIdx].pt)
    src_pts = np.int32(src_pts)
    dst_pts = np.int32(dst_pts)
    F, mask = cv2.findFundamentalMat(src_pts, dst_pts, cv2.FM_LMEDS)
    # We select only inlier points
    src_pts = src_pts[mask.ravel() == 1]
    dst_pts = dst_pts[mask.ravel() == 1]

    # # 计算极线
    # img11 = img1
    # img22 = img2
    # # Find epilines corresponding to points in right image (second image) and
    # # drawing its lines on left image
    # lines1 = cv2.computeCorrespondEpilines(dst_pts.reshape(-1, 1, 2), 2, F)
    # lines1 = lines1.reshape(-1, 3)
    # img5, img6 = drawlines(img11, img22, lines1, src_pts, dst_pts)
    # # Find epilines corresponding to points in left image (first image) and
    # # drawing its lines on right image
    # lines2 = cv2.computeCorrespondEpilines(src_pts.reshape(-1, 1, 2), 1, F)
    # lines2 = lines2.reshape(-1, 3)
    # img3, img4 = drawlines(img22, img11, lines2, dst_pts, src_pts)
    # plt.subplot(121), plt.imshow(img5)
    # plt.subplot(122), plt.imshow(img3)
    # plt.show()

    # 极线校正
    rectified_results = cv2.stereoRectifyUncalibrated(
        src_pts.reshape(-1, 2), dst_pts.reshape(-1, 2), F, img1.shape[:2]
    )
    rectified_img1 = rectified_results[1]
    rectified_img2 = rectified_results[2]
    rectified_img1 = cv2.warpPerspective(img1, rectified_img1, img1.shape[:2][::-1])
    rectified_img2 = cv2.warpPerspective(img2, rectified_img2, img2.shape[:2][::-1])
    plt.subplot(121), plt.imshow(rectified_img1)
    plt.subplot(122), plt.imshow(rectified_img2)
    plt.show()