python opencv 多张图像拼接

可以使用OpenCV中的warpPerspective()方法和findHomography()方法来实现多张图像的拼接。具体步骤如下：

1.读取多张图像并提取它们的特征点。

2.对于每两张图像，使用OpenCV中的findHomography()方法计算它们之间的单应矩阵。

3.使用OpenCV中的warpPerspective()方法将每张图像转换为拼接后的图像中的位置。

4.将所有转换后的图像拼接在一起。

下面是一个示例代码，假设我们有三张图像im1、im2和im3，它们已经被读取并且我们已经计算出了它们之间的单应矩阵h12、h23和h31：

import cv2
import numpy as np

# 读取图像
im1 = cv2.imread('image1.jpg')
im2 = cv2.imread('image2.jpg')
im3 = cv2.imread('image3.jpg')

# 提取特征点
detector = cv2.xfeatures2d.SIFT_create()
matcher = cv2.FlannBasedMatcher({'algorithm': 0, 'trees': 5}, {})

kpts1, desc1 = detector.detectAndCompute(im1, None)
kpts2, desc2 = detector.detectAndCompute(im2, None)
kpts3, desc3 = detector.detectAndCompute(im3, None)

matches12 = matcher.knnMatch(desc1, desc2, 2)
matches23 = matcher.knnMatch(desc2, desc3, 2)
matches31 = matcher.knnMatch(desc3, desc1, 2)

# 计算单应矩阵
pts1 = []
pts2 = []
for m in matches12:
    if len(m) == 2 and m[0].distance < m[1].distance * 0.7:
        pts1.append(kpts1[m[0].queryIdx].pt)
        pts2.append(kpts2[m[0].trainIdx].pt)

pts1 = np.array(pts1)
pts2 = np.array(pts2)

h12, status = cv2.findHomography(pts1, pts2, cv2.RANSAC, 5.0)

pts1 = []
pts2 = []
for m in matches23:
    if len(m) == 2 and m[0].distance < m[1].distance * 0.7:
        pts1.append(kpts2[m[0].queryIdx].pt)
        pts2.append(kpts3[m[0].trainIdx].pt)

pts1 = np.array(pts1)
pts2 = np.array(pts2)

h23, status = cv2.findHomography(pts1, pts2, cv2.RANSAC, 5.0)

pts1 = []
pts2 = []
for m in matches31:
    if len(m) == 2 and m[0].distance < m[1].distance * 0.7:
        pts1.append(kpts3[m[0].queryIdx].pt)
        pts2.append(kpts1[m[0].trainIdx].pt)

pts1 = np.array(pts1)
pts2 = np.array(pts2)

h31, status = cv2.findHomography(pts1, pts2, cv2.RANSAC, 5.0)

# 转换图像
size = (im1.shape[1] + im2.shape[1] + im3.shape[1], im1.shape[0])
im12 = cv2.warpPerspective(im1, h12, size)
im23 = cv2.warpPerspective(im2, np.dot(h23, h12), size)
im31 = cv2.warpPerspective(im3, np.dot(h31, np.dot(h23, h12)), size)

# 拼接图像
result = np.zeros((im1.shape[0], size[0], 3), dtype=np.uint8)
result[:, :im1.shape[1], :] = im1
result[:, im1.shape[1]:im1.shape[1] + im2.shape[1], :] = im2
result[:, im1.shape[1] + im2.shape[1]:, :] = im3

mask = np.zeros((im1.shape[0], size[0]), dtype=np.uint8)
mask[:, :im1.shape[1]] = 255
mask = cv2.warpPerspective(mask, h12, size)
result = cv2.seamlessClone(im12, result, mask, (im1.shape[1], 0), cv2.NORMAL_CLONE)

mask = np.zeros((im1.shape[0], size[0]), dtype=np.uint8)
mask[:, :im1.shape[1] + im2.shape[1]] = 255
mask = cv2.warpPerspective(mask, np.dot(h23, h12), size)
result = cv2.seamlessClone(im23, result, mask, (im1.shape[1] + im2.shape[1], 0), cv2.NORMAL_CLONE)

mask = np.zeros((im1.shape[0], size[0]), dtype=np.uint8)
mask[:, im1.shape[1]:] = 255
mask = cv2.warpPerspective(mask, np.dot(h31, np.dot(h23, h12)), size)
result = cv2.seamlessClone(im31, result, mask, (im1.shape[1] + im2.shape[1], 0), cv2.NORMAL_CLONE)

# 显示结果
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()