使用opencv python进行全景图像拼接

pip install opencv-python

import cv2
import numpy as np

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

sift = cv2.SIFT_create()

kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

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)

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

if len(good_matches) > 4:
    src_pts = np.float32([kp1[m.queryIdx].pt for m in good_matches]).reshape(-1, 2)
    dst_pts = np.float32([kp2[m.trainIdx].pt for m in good_matches]).reshape(-1, 2)

    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
else:
    print("Not enough matches are found - {}/{}".format(len(good_matches), 4))
    M = None

h1, w1 = img1.shape[:2]
h2, w2 = img2.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)

dst_pts1 = cv2.perspectiveTransform(pts1, M)
dst_pts2 = cv2.perspectiveTransform(pts2, M)

dst_pts = np.concatenate((dst_pts1, dst_pts2), axis=0)
[xmin, ymin] = np.int32(dst_pts.min(axis=0).ravel() - 0.5)
[xmax, ymax] = np.int32(dst_pts.max(axis=0).ravel() + 0.5)

t = [-xmin, -ymin]
Ht = np.array([[1, 0, t[0]], [0, 1, t[1]], [0, 0, 1]])

result = cv2.warpPerspective(img2, Ht.dot(M), (xmax-xmin, ymax-ymin))
result[t[1]:h1+t[1], t[0]:w1+t[0]] = img1

cv2.imshow('Panorama', result)
cv2.waitKey(0)
cv2.destroyAllWindows()