在我的previous question中,我了解到我必须安装opencv-contrib
才能将OpenCV Python与外部模块(如SIFT)一起使用。但是,在我的项目中,我想使用ORB或类似的东西。 cv2.ORB()
不起作用,cv2.xfeatures2d.ORB_create()
或任何其他命令的粘合也不起作用。
据了解,OpenCV的Python API文档相当差。
如何使用ORB匹配OpenCV Python中的功能?
MWE:
#!/usr/bin/python2.7
import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread('smallburger.jpg',0)
# Initiate STAR detector
orb = cv2.ORB()
# find the keypoints with ORB
kp = orb.detect(img,None)
# compute the descriptors with ORB
kp, des = orb.compute(img, kp)
# draw only keypoints location,not size and orientation
img2 = cv2.drawKeypoints(img,kp,color=(0,255,0), flags=0)
plt.imshow(img2),plt.show()
CLI输出:
Traceback (most recent call last):
File "./mwe.py", line 9, in <module>
orb = cv2.ORB()
AttributeError: 'module' object has no attribute 'ORB'
答案 0 :(得分:9)
愚蠢的OpenCV。 It's just cv2.ORB_create()
.
答案 1 :(得分:1)
这里有我的培训代码
def featureMatchingBF(self,img1,img2,method):
corners = cv2.goodFeaturesToTrack(img1, 7, 0.05, 25)
corners = np.float32(corners)
for item in corners:
x, y = item[0]
cv2.circle(img1, (x,y), 5, (255,0,0))
cv2.imshow("Top 'k' features", img1)
cv2.waitKey()
#=======================================================================
# (H1, hogImage1) = feature.hog(img1, orientations=9, pixels_per_cell=(6, 6),
# cells_per_block=(2, 2), transform_sqrt=True, visualise=True)
# hogImage1 = exposure.rescale_intensity(hogImage1, out_range=(0, 255))
# hogImage1 = hogImage1.astype("uint8")
# cv2.imshow("Input:",img1)
# cv2.imshow("HOG Image", hogImage1)
# cv2.waitKey(0)
#=======================================================================
if method is "ORB":
#Compute keypoints for both images
kp1,des1 = self.computeORB(img1)
kp2,des2 = self.computeORB(img2)
#===================================================================
# for i,j in zip(kp1,kp2):
# print("KP1:",i.pt)
# print("KP2:",j.pt)
#===================================================================
#use brute force matcher for matching descriptor1 and descriptor2
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
# Match descriptors.
matches = bf.match(des1,des2)
# Sort them in the order of their distance.
matches = sorted(matches, key = lambda x:x.distance)
self.filterMatches(matches)
# Draw first 10 matches.
img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches[:20], flags=2,outImg = img1)
#show result
cv2.imshow("Matches",img3)
cv2.waitKey(0)
def computeORB(self,img):
#Initiate ORB detector
orb = cv2.ORB_create()
#find keypoints
kp = orb.detect(img,None)
#compute despriptor
kp, des = orb.compute(img,kp)
# draw only keypoints location,not size and orientation
img2 = cv2.drawKeypoints(img, kp, None, color=(0,255,0), flags=0)
#plt.imshow(img2), plt.show()
return kp,des