OpenCV 2.4.1 - 在 Python 中计算 SURF 描述符 [英] OpenCV 2.4.1 - computing SURF descriptors in Python

问题描述

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(冲浪").但是，在检测到关键点后，我无法获取描述符.调用 SURF.detect 的正确方法是什么?

I'm trying to update my code to use cv2.SURF() as opposed to cv2.FeatureDetector_create("SURF") and cv2.DescriptorExtractor_create("SURF"). However I'm having trouble getting the descriptors after detecting the keypoints. What's the correct way to call SURF.detect?

我尝试遵循 OpenCV 文档，但我有点困惑.这是它在文档中所说的.

I tried following the OpenCV documentation, but I'm a little confused. This is what it says in the documentation.

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

如何在第二次调用 SURF.detect 时传入关键点?

How do I pass the keypoints in when making the second call to SURF.detect?

推荐答案

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一个匹配 SURF 关键点的样本，下面是一个非常简单和基本的，类似于模板匹配:

I am not sure whether i understand your questions correctly. But if you are looking for a sample of matching SURF keypoints, a very simple and basic one is below, which is similar to template matching:

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):

Below are the results I got (copy pasted template image on original image using paint):

如您所见，存在一些小错误.但对于初创公司，希望一切顺利.

As you can see, there are some small mistakes. But for a startup, hope it is OK.

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