算法效果图

相比于之前的FLANN，这里还标出了对应的图形。
（大家也可以自己拍一些照片来试试效果）

代码

import cv2
from matplotlib import pyplot as plt
import numpy as np

MIN_MATCH_COUNT = 10

img1 = cv2.imread('6.jpg', 0)
img2 = cv2.imread('7.jpg', 0)

sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

FLANN_INDEX_KDTREE = 0
indexParams = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
searchParams = dict(checks=50)

flann = cv2.FlannBasedMatcher(indexParams, searchParams)

matches = flann.knnMatch(des1, des2, k=2)

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

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)

    matchesMask = mask.ravel().tolist()
    h, w = img1.shape
    pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)
    dst = cv2.perspectiveTransform(pts, M)

    img2 = cv2.polylines(img2, [np.int32(dst)], True, 255, 3, cv2.LINE_AA)
else:
    print("Not Enough")
    matchesMask = None

drawParams = dict(matchColor=(0, 255, 0),
                  singlePointColor=None,
                  matchesMask=matchesMask,
                  flags=2
                  )
resultImage = cv2.drawMatches(img1, kp1, img2, kp2, good, None, **drawParams)
plt.xticks([]), plt.yticks([])
plt.imshow(resultImage), plt.show()