我不确定我是否正确理解您的问题。但是,如果您正在寻找匹配的SURF关键点示例,则下面是一个非常简单和基本的示例,与模板匹配类似:
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, 这里有一些小错误 。但是对于一家初创公司,希望它可以。
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我正在尝试更新代码以使用
cv2.SURF()
而不是cv2.FeatureDetector_create("SURF")
和cv2.DescriptorExtractor_create("SURF")
。但是我在检测到关键点后很难获取描述符。调用SURF.detect
的正确方法是SURF.detect
?我尝试遵循OpenCV文档,但是我有些困惑。这就是文档中所说的。
在第二次调用
SURF.detect
时如何传递关键点?