我尝试使用opencv获取一个查询图像并在一个基本图像中进行匹配。我看了一下在线教程，你看，他们有示例代码来做这件事。所以我复制并粘贴了代码，并尝试用一些试用图像来运行它。下面是代码和一组图像示例。在import numpy as np

import cv2

from matplotlib import pyplot as plt

MIN_MATCH_COUNT = 10

img1 = cv2.imread('10hearts.png',0) # queryImage

img2 = cv2.imread('example.jpg',0) # trainImage

# Initiate SIFT detector

sift = cv2.xfeatures2d.SIFT_create()

# find the keypoints and descriptors with SIFT

kp1, des1 = sift.detectAndCompute(img1,None)

kp2, des2 = sift.detectAndCompute(img2,None)

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)

# store all the good matches as per Lowe's ratio test.

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 matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT)

matchesMask = None

draw_params = dict(matchColor = (0,255,0), # draw matches in green color

singlePointColor = None,

matchesMask = matchesMask, # draw only inliers

flags = 2)

img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)

plt.imshow(img3, 'gray'),plt.show()

以下是我的查询图像：

我正在搜索的图像：

不幸的是，将结果可视化的代码永远不会起作用。每次运行它时，都会出现以下错误：

^{pr2}$

我做了一些简单的调试，在findHomeography()调用之前，一切似乎都很好，它返回Null，Null。有什么建议吗？我按照以下说明下载opencv，以防出现问题：http://www.pyimagesearch.com/2015/06/22/install-opencv-3-0-and-python-2-7-on-ubuntu/