OpenCV 2.4.1 - 在 Python 中计算 SURF 描述符

我正在尝试更新我的代码以使用cv2.SURF()相对于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。正确的打电话方式是什么SURF.detect?

我尝试遵循 OpenCV 文档，但我有点困惑。这就是文档中所说的。

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你的问题。但是，如果您正在寻找匹配 SURF 关键点的示例，下面是一个非常简单且基本的示例，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（使用绘画将粘贴的模板图像复制到原始图像上）：

如你看到的，有一些小错误。但对于一家初创公司来说，希望一切顺利。