使用python实现基于sift特征提取的图像拼接的代码示例

以下是一个基于SIFT特征提取的图像拼接代码示例，使用了OpenCV库：

import cv2
import numpy as np

def stitch_images(img1, img2):
    # Convert images to grayscale
    gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
    gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

    # Initialize SIFT detector
    sift = cv2.xfeatures2d.SIFT_create()

    # Find keypoints and descriptors for both images
    kp1, des1 = sift.detectAndCompute(gray1, None)
    kp2, des2 = sift.detectAndCompute(gray2, None)

    # Initialize FLANN matcher
    flann = cv2.FlannBasedMatcher()

    # Match keypoints using FLANN matcher
    matches = flann.knnMatch(des1, des2, k=2)

    # Filter matches using Lowe's ratio test
    good_matches = []
    for m, n in matches:
        if m.distance < 0.7 * n.distance:
            good_matches.append(m)

    # Find homography matrix 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)
    H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)

    # Warp img1 to img2 using homography matrix
    h, w = gray1.shape
    warped_img1 = cv2.warpPerspective(img1, H, (w, h))

    # Create a mask for img2
    mask = np.zeros((h, w), dtype=np.uint8)
    cv2.fillConvexPoly(mask, np.int32([kp2[m.trainIdx].pt for m in good_matches]), (255, 255, 255), cv2.LINE_AA)

    # Blend images using mask
    blended_img = cv2.seamlessClone(warped_img1, img2, mask, (w//2, h//2), cv2.NORMAL_CLONE)

    return blended_img

使用方法：

img1 = cv2.imread('image1.jpg')
img2 = cv2.imread('image2.jpg')
result = stitch_images(img1, img2)
cv2.imshow('Result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

请注意，这只是一个简单的示例，可能需要进行调整和改进以适应不同的图像。