我正在尝试更新我的代码以使用cv2.SURF()而不是cv2.FeatureDetector_create("SURF")and cv2.DescriptorExtractor_create("SURF")。但是，在检测到关键点后，我无法获取描述符。正确的调用方式是SURF.detect什么？
我尝试遵循 OpenCV 文档，但我有点困惑。这就是它在文档中所说的。
Python: cv2.SURF.detect(img, mask) → keypoints¶
Python: cv2.SURF.detect(img, mask[, descriptors[, useProvidedKeypoints]]) → keypoints, descriptors
第二次调用时如何传递关键点SURF.detect？
我不确定我是否正确理解了您的问题。但是如果你正在寻找匹配 SURF 关键点的样本，下面是一个非常简单和基本的样本，它类似于模板匹配：
import cv2
import numpy as np
# Load the images
img =cv2.imread('messi4.jpg')
# Convert them to grayscale
imgg =cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# SURF extraction
surf = cv2.SURF()
kp, descritors = surf.detect(imgg,None,useProvidedKeypoints = False)
# Setting up samples and responses for kNN
samples = np.array(descritors)
responses = np.arange(len(kp),dtype = np.float32)
# kNN training
knn = cv2.KNearest()
knn.train(samples,responses)
# Now loading a template image and searching for similar keypoints
template = cv2.imread('template.jpg')
templateg= cv2.cvtColor(template,cv2.COLOR_BGR2GRAY)
keys,desc = surf.detect(templateg,None,useProvidedKeypoints = False)
for h,des in enumerate(desc):
des = np.array(des,np.float32).reshape((1,128))
retval, results, neigh_resp, dists = knn.find_nearest(des,1)
res,dist = int(results[0][0]),dists[0][0]
if dist<0.1: # draw matched keypoints in red color
color = (0,0,255)
else: # draw unmatched in blue color
print dist
color = (255,0,0)
#Draw matched key points on original image
x,y = kp[res].pt
center = (int(x),int(y))
cv2.circle(img,center,2,color,-1)
#Draw matched key points on template image
x,y = keys[h].pt
center = (int(x),int(y))
cv2.circle(template,center,2,color,-1)
cv2.imshow('img',img)
cv2.imshow('tm',template)
cv2.waitKey(0)
cv2.destroyAllWindows()
以下是我得到的结果（使用油漆将粘贴的模板图像复制到原始图像上）：
如您所见，有一些小错误。但对于一家初创公司来说，希望它是好的。
上述算法的一个改进是：
import cv2
import numpy
opencv_haystack =cv2.imread('haystack.jpg')
opencv_needle =cv2.imread('needle.jpg')
ngrey = cv2.cvtColor(opencv_needle, cv2.COLOR_BGR2GRAY)
hgrey = cv2.cvtColor(opencv_haystack, cv2.COLOR_BGR2GRAY)
# build feature detector and descriptor extractor
hessian_threshold = 85
detector = cv2.SURF(hessian_threshold)
(hkeypoints, hdescriptors) = detector.detect(hgrey, None, useProvidedKeypoints = False)
(nkeypoints, ndescriptors) = detector.detect(ngrey, None, useProvidedKeypoints = False)
# extract vectors of size 64 from raw descriptors numpy arrays
rowsize = len(hdescriptors) / len(hkeypoints)
if rowsize > 1:
hrows = numpy.array(hdescriptors, dtype = numpy.float32).reshape((-1, rowsize))
nrows = numpy.array(ndescriptors, dtype = numpy.float32).reshape((-1, rowsize))
#print hrows.shape, nrows.shape
else:
hrows = numpy.array(hdescriptors, dtype = numpy.float32)
nrows = numpy.array(ndescriptors, dtype = numpy.float32)
rowsize = len(hrows[0])
# kNN training - learn mapping from hrow to hkeypoints index
samples = hrows
responses = numpy.arange(len(hkeypoints), dtype = numpy.float32)
#print len(samples), len(responses)
knn = cv2.KNearest()
knn.train(samples,responses)
# retrieve index and value through enumeration
for i, descriptor in enumerate(nrows):
descriptor = numpy.array(descriptor, dtype = numpy.float32).reshape((1, rowsize))
#print i, descriptor.shape, samples[0].shape
retval, results, neigh_resp, dists = knn.find_nearest(descriptor, 1)
res, dist = int(results[0][0]), dists[0][0]
#print res, dist
if dist < 0.1:
# draw matched keypoints in red color
color = (0, 0, 255)
else:
# draw unmatched in blue color
color = (255, 0, 0)
# draw matched key points on haystack image
x,y = hkeypoints[res].pt
center = (int(x),int(y))
cv2.circle(opencv_haystack,center,2,color,-1)
# draw matched key points on needle image
x,y = nkeypoints[i].pt
center = (int(x),int(y))
cv2.circle(opencv_needle,center,2,color,-1)
cv2.imshow('haystack',opencv_haystack)
cv2.imshow('needle',opencv_needle)
cv2.waitKey(0)
cv2.destroyAllWindows()
您可以取消注释打印语句以更好地了解所使用的数据结构。