OpenCV 2.4.1 - 在Python中计算SURF描述符

Question

我正在尝试更新我的代码以使用cv2.SURF()而不是cv2.FeatureDetector_create("SURF")和cv2.DescriptorExtractor_create("SURF").但是在检测到关键点后我无法获取描述符.什么是正确的打电话方式SURF.detect？

我试过跟随OpenCV文档,但我有点困惑.这就是它在文档中所说的内容.

Python: cv2.SURF.detect(img, mask) ? keypoints¶
Python: cv2.SURF.detect(img, mask[, descriptors[, useProvidedKeypoints]]) ? keypoints, descriptors

在进行第二次调用时如何传递关键点SURF.detect？

Answer 1

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和基本的关键点,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()

下面是我得到的结果(使用paint在原始图像上复制粘贴的模板图像):

如你所见,有一些小错误.但对于创业公司来说,希望没关系.