OpenCV，功能与教程中的代码匹配 [英] OpenCV, feature matching with code from the tutorial

问题描述

我复制了特征匹配的代码使用来自 OpenCV 教程页面的 FLANN，并进行了以下更改:

I copied the code of the Feature Matching with FLANN from the OpenCV tutorial page, and made the following changes:

• 我使用了 SIFT 功能，而不是 SURF；

• 我修改了良好匹配"的检查.而不是

• I used the SIFT features, instead of SURF;

• I modified the check for a 'good match'. Instead of

if( matches[i].distance < 2*min_dist )

我用过

    if( matches[i].distance <= 2*min_dist )

否则在将图像与其自身进行比较时，我会得到零个良好匹配.

otherwise I would get zero good matches when comparing an image with itself.

• 在绘制关键点时修改了参数:

• Modified parameter in drawing the keypoints:

drawMatches( img1, k1, img2, k2,
                 good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
                 vector<char>(), DrawMatchesFlags::DEFAULT);

我从INRIA-Holidays数据集.然后我将每个图像与所有其他图像进行比较并绘制匹配项.

I extracted the SIFT from all the images in the folder Ireland of the INRIA-Holidays dataset. Then I compared each image to all the others and draw the matches.

然而，我在过去使用的任何其他 SIFT/Matcher 实现中从未遇到过一个奇怪的问题:

However there is a strange problem I have never experienced with any other SIFT/Matcher implementation I used in the past:

• 我与自己匹配的图像的匹配很好.除了一些关键点之外，每个关键点都映射到自身上.见上图.
• 当我将 I 与另一个图像 J(J 不等于 I)进行匹配时，许多点被映射到同一点上.下面是一些示例.

是否有人使用了 OpenCV 教程中的相同代码并报告了与我不同的体验?

Is there anyone who used the same code from the OpenCV tutorial and can report a different experience from mine?

推荐答案

查看 matcher_simple.cpp 示例.它使用了一个蛮力匹配器，似乎工作得很好.代码如下:

Checkout the matcher_simple.cpp example. It uses a brute force matcher that seems to work pretty well. Here is the code:

// detecting keypoints
SurfFeatureDetector detector(400);
vector<KeyPoint> keypoints1, keypoints2;
detector.detect(img1, keypoints1);
detector.detect(img2, keypoints2);

// computing descriptors
SurfDescriptorExtractor extractor;
Mat descriptors1, descriptors2;
extractor.compute(img1, keypoints1, descriptors1);
extractor.compute(img2, keypoints2, descriptors2);

// matching descriptors
BFMatcher matcher(NORM_L2);
vector<DMatch> matches;
matcher.match(descriptors1, descriptors2, matches);

// drawing the results
namedWindow("matches", 1);
Mat img_matches;
drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches);
imshow("matches", img_matches);
waitKey(0);

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