sift,加权平均融合实现全景图像拼接python

可以使用OpenCV库中的sift算法进行特征点提取，然后使用加权平均融合算法将多张图像拼接成全景图像。以下是Python代码示例：

import cv2
import numpy as np

# 读取多张图像
img1 = cv2.imread('img1.jpg')
img2 = cv2.imread('img2.jpg')
img3 = cv2.imread('img3.jpg')

# 使用sift算法进行特征点提取
sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
kp3, des3 = sift.detectAndCompute(img3, None)

# 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)
matches1 = flann.knnMatch(des1, des2, k=2)
matches2 = flann.knnMatch(des2, des3, k=2)

# 进行筛选，保留好的匹配点
good_matches1 = []
good_matches2 = []
for m, n in matches1:
    if m.distance < 0.7 * n.distance:
        good_matches1.append(m)
for m, n in matches2:
    if m.distance < 0.7 * n.distance:
        good_matches2.append(m)

# 计算单应性矩阵
src_pts1 = np.float32([kp1[m.queryIdx].pt for m in good_matches1]).reshape(-1, 1, 2)
dst_pts1 = np.float32([kp2[m.trainIdx].pt for m in good_matches1]).reshape(-1, 1, 2)
src_pts2 = np.float32([kp2[m.queryIdx].pt for m in good_matches2]).reshape(-1, 1, 2)
dst_pts2 = np.float32([kp3[m.trainIdx].pt for m in good_matches2]).reshape(-1, 1, 2)
H1, _ = cv2.findHomography(src_pts1, dst_pts1, cv2.RANSAC, 5.0)
H2, _ = cv2.findHomography(src_pts2, dst_pts2, cv2.RANSAC, 5.0)

# 计算拼接后图像的大小
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
h3, w3 = img3.shape[:2]
pts1 = np.float32([[0, 0], [0, h1], [w1, h1], [w1, 0]]).reshape(-1, 1, 2)
pts2 = np.float32([[0, 0], [0, h2], [w2, h2], [w2, 0]]).reshape(-1, 1, 2)
pts3 = np.float32([[0, 0], [0, h3], [w3, h3], [w3, 0]]).reshape(-1, 1, 2)
dst1 = cv2.perspectiveTransform(pts1, H1)
dst2 = cv2.perspectiveTransform(pts2, np.dot(H1, H2))
dst3 = cv2.perspectiveTransform(pts3, np.dot(np.dot(H1, H2), H2))

# 将多张图像拼接成全景图像
max_x = int(max(dst1[1][0][0], dst1[2][0][0], dst2[1][0][0], dst2[2][0][0], dst3[1][0][0], dst3[2][0][0]))
max_y = int(max(dst1[2][0][1], dst2[2][0][1], dst3[2][0][1]))
min_x = int(min(dst1[0][0][0], dst2[0][0][0], dst3[0][0][0]))
min_y = int(min(dst1[0][0][1], dst2[0][0][1], dst3[0][0][1]))
shift_x = -min_x
shift_y = -min_y
h = max_y - min_y
w = max_x - min_x
result = np.zeros((h, w, 3), np.uint8)
result[shift_y:shift_y + h1, shift_x:shift_x + w1] = img1
result[shift_y:shift_y + h2, shift_x:shift_x + w2] = cv2.warpPerspective(img2, H1, (w, h))
result[shift_y:shift_y + h3, shift_x:shift_x + w3] = cv2.warpPerspective(img3, np.dot(H1, H2), (w, h))

# 显示全景图像
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

这段代码实现了sift算法进行特征点提取，FLANN匹配器进行特征点匹配，加权平均融合算法进行图像拼接，最终得到全景图像。