我正在尝试在Fedora 22下使用SIFT检测器,使用Python 2.7.10和OpenCV 2.4.12。至于安装,我所做的只是sudo dnf install opencv*
并让它安装所有依赖项,但是在尝试运行下面的示例代码时,我遇到了错误:
Traceback (most recent call last):
File "features.py", line 47, in <module>
sift = cv2.SIFT()
AttributeError: 'module' object has no attribute 'SIFT'
我认为这意味着Python无法找到SIFT库,我是否正确?我该如何解决这个问题?
示例代码:
import numpy as np
import cv2
from matplotlib import pyplot as plt
MIN_MATCH_COUNT = 10
img1 = cv2.imread('poste_blur.jpg',0) # queryImage
img2 = cv2.imread('poste.jpg',0) # trainImage
# Initiate SIFT detector
sift = cv2.SIFT()
# 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()
答案 0 :(得分:0)
正如berak所建议的,如果你需要使用SURF或SIFT,你需要从源代码编译OpenCV。你绝对应该首先卸载当前版本。
我认为在Fedora上你可以使用类似的东西:
sudo dnf erase opencv*
我还会执行sudo updatedb
和locate opencv
来查找所有剩余的OpenCV文件。然后,您应该手动删除它们。