BoW在OpenCV中使用预先计算的功能 [英] BoW in OpenCV using precomputed features

问题描述

我需要做BOW（一袋字），但我只有描述的图像的关键点。目前，我已经获得了词汇表：

I need to do BOW (bag of words) but I only have the described keypoints of the images. For the moment, I have obtained the vocabulary using:

cv::BOWKMeansTrainer bowtrainerCN(numCenters); //num clusters
bowtrainerCN.add(allDescriptors);
cv::Mat vocabularyCN = bowtrainerCN.cluster();

现在我需要做赋值，但是我不能使用compute函数，因为它计算描述符的图像和我已经有了。

So now I need to do the assignment but I can't use the compute function because it calculates the descriptors of the images and I already have that. Is there any function to do the assignment or have I to compute it manually?

推荐答案

一旦你构建了词汇表（codebook）使用 cv :: BOWKMeansTrainer :: cluster（）方法，然后可以将描述符（具有适当的大小和类型）匹配到码本。你首先必须选择你需要的匹配器类型使用规范。 （请参阅 opencv doc ）

Once you have built the vocabulary (codebook) using cv::BOWKMeansTrainer::cluster() method, you can then match a descriptor (with suitable size and type) to the codebook. You first have to choose the type of matcher you need with a norm to use. (see opencv doc)

例如， cv :: BFMatcher 和 L2 norm

// init the matcher with you pre-trained codebook
cv::Ptr<cv::DescriptorMatcher > matcher = new cv::BFMatcher(cv::NORM_L2);
matcher->add(std::vector<cv::Mat>(1, vocabulary));
// matches
std::vector<cv::DMatch> matches;
matcher->match(new_descriptors,matches);

然后，你的codebook中的new_descriptors [i]的最接近的代码字的索引将是

Then the index of the closest codeword in your codebook for the new_descriptors[i] will be

matches[i].trainIdx; 

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