OpenCV如何实现图像拼接

OpenCV可以通过以下步骤实现图像拼接：
1. 读取需要拼接的图像。

import cv2
img1 = cv2.imread('image1.jpg')
img2 = cv2.imread('image2.jpg')

2. 将两张图片转换为灰度图像。

gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

3. 使用SIFT算法检测关键点和计算描述符。

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

4. 使用FLANN匹配器进行特征点匹配。

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)

5. 根据匹配结果筛选出优秀的匹配点。

good = []
for m, n in matches:
    if m.distance < 0.7 * n.distance:
        good.append(m)

6. 计算变换矩阵。

MIN_MATCH_COUNT = 10
if len(good) > MIN_MATCH_COUNT:
    src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
    dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
else:
    print("Not enough matches are found - %d/%d" % (len(good), MIN_MATCH_COUNT))

7. 进行图像拼接。

result = cv2.warpPerspective(img1, M, (img1.shape[1] + img2.shape[1], img1.shape[0]))
result[0:img2.shape[0], 0:img2.shape[1]] = img2