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Error on cv::cuda::DescriptorMatcher::knnMatch

asked 2015-04-26 14:48:16 -0500

krips89 gravatar image

While trying to use the knnMatch function cuda::DescriptorMatcher using the documentation provided here: I am getting the following error:

OpenCV Error: The function/feature is not implemented (getGpuMat is available only for cuda::GpuMat and cuda::HostMem) in getGpuMat, file /home/sarkar/opencv/opencv/modules/core/src/matrix.cpp, line 1419
terminate called after throwing an instance of 'cv::Exception'
  what():  /home/sarkar/opencv/opencv/modules/core/src/matrix.cpp:1419: error: (-213) getGpuMat is available only for cuda::GpuMat and cuda::HostMem in function getGpuMat

Any idea what it means? I am using a very simple code like the following:

matcher_gpu_->knnMatch(descriptors_frame, descriptors_model, matches, 2);

Where descriptor_frame, descriptor_model are cv::Mat; and matches is vector of vector of DMatch.

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answered 2015-06-08 17:24:05 -0500

Eduardo gravatar image

updated 2015-06-08 17:30:51 -0500

I hope the original poster already solved his problem, but the error highlighted is that you have to supply the descriptors using cv::cuda::GpuMat and not with cv::Mat, as you use the GPU matcher class.

Nevertheless, I post here 2 example codes (in OpenCV 3.0) to achieve ORB detection/extraction and descriptors matching using the CUDA module which, I hope, could be helpful to someone else:

  • example_with_full_gpu(): detect ORB keypoints, compute ORB descriptors and perform the knn-matching with only calls to cuda functions
  • example_with_gpu_matching(): only the matching use the GPU, to demonstrate that it is possible to use all the features available in features2d.hpp or xfeatures2d.hpp and match with the GPU

    #include <iostream>
    #include <opencv2/opencv.hpp>
    #include <opencv2/core/cuda.hpp>
    #include <opencv2/cudaimgproc.hpp>
    #include <opencv2/cudafeatures2d.hpp>
    
    void example_with_full_gpu(const cv::Mat &img1, const cv::Mat img2) {
    //Upload from host memory to gpu device memeory
    cv::cuda::GpuMat img1_gpu(img1), img2_gpu(img2);
    cv::cuda::GpuMat img1_gray_gpu, img2_gray_gpu;
    
    //Convert RGB to grayscale as gpu detectAndCompute only allow grayscale GpuMat
    cv::cuda::cvtColor(img1_gpu, img1_gray_gpu, CV_BGR2GRAY);
    cv::cuda::cvtColor(img2_gpu, img2_gray_gpu, CV_BGR2GRAY);
    
    //Create a GPU ORB feature object
    //blurForDescriptor=true seems to give better results
    //http://answers.opencv.org/question/10835/orb_gpu-not-as-good-as-orbcpu/
    cv::Ptr<cv::cuda::ORB> orb = cv::cuda::ORB::create(500, 1.2f, 8, 31, 0, 2, 0, 31, 20, true);
    
    cv::cuda::GpuMat keypoints1_gpu, descriptors1_gpu;
    //Detect ORB keypoints and extract descriptors on train image (box.png)
    orb->detectAndComputeAsync(img1_gray_gpu, cv::cuda::GpuMat(), keypoints1_gpu, descriptors1_gpu);
    std::vector<cv::KeyPoint> keypoints1;
    //Convert from CUDA object to std::vector<cv::KeyPoint>
    orb->convert(keypoints1_gpu, keypoints1);
    std::cout << "keypoints1=" << keypoints1.size() << " ; descriptors1_gpu=" << descriptors1_gpu.rows 
        << "x" << descriptors1_gpu.cols << std::endl;
    
    std::vector<cv::KeyPoint> keypoints2;
    cv::cuda::GpuMat descriptors2_gpu;
    //Detect ORB keypoints and extract descriptors on query image (box_in_scene.png)
    //The conversion from internal data to std::vector<cv::KeyPoint> is done implicitly in detectAndCompute()
    orb->detectAndCompute(img2_gray_gpu, cv::cuda::GpuMat(), keypoints2, descriptors2_gpu);
    std::cout << "keypoints2=" << keypoints2.size() << " ; descriptors2_gpu=" << descriptors2_gpu.rows 
        << "x" << descriptors2_gpu.cols << std::endl;
    
    //Create a GPU brute-force matcher with Hamming distance as we use a binary descriptor (ORB)
    cv::Ptr<cv::cuda::DescriptorMatcher> matcher = cv::cuda::DescriptorMatcher::createBFMatcher(cv::NORM_HAMMING);
    
    std::vector<std::vector<cv::DMatch> > knn_matches;
    //Match each query descriptor to a train descriptor
    matcher->knnMatch(descriptors2_gpu, descriptors1_gpu, knn_matches, 2);
    std::cout << "knn_matches=" << knn_matches.size() << std::endl;
    
    std::vector<cv::DMatch> matches;
    //Filter the matches using the ratio test
    for(std::vector<std::vector<cv::DMatch> >::const_iterator it = knn_matches.begin(); it != knn_matches.end(); ++it) {
        if(it->size() > 1 && (*it)[0].distance/(*it)[1].distance < 0.8) {
            matches.push_back((*it)[0]);
        }
    }
    
    cv::Mat imgRes;
    //Display and save the image with matches
    cv::drawMatches(img2, keypoints2, img1, keypoints1, matches, imgRes);
    cv::imshow("imgRes", imgRes);
    cv::imwrite("GPU_ORB-matching.png", imgRes);
    
    cv::waitKey(0); 
    }
    
    void example_with_gpu_matching(const cv::Mat &img1, const cv::Mat img2) {
    //Create a CPU ORB feature object
    cv::Ptr<cv::Feature2D> orb = cv::ORB::create(500, 1.2f, 8, 31, 0, 2, 0 ...
(more)
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Hi, Hope we can use knnMatch with brute force matcher also. When I use knnmatch with either 1 or 2 as the 4th argument, I still receive matched keypoints equal to querydescriptor size only. In this case, the traindescriptors are much smaller than the querydescriptor. In BFMatcher (without cuda), crosscheck option is present, enabling it as true, ensure only the best matches are listed out. Cuda BF Matcher doesnt seem to have Crosscheck option available in its API. Am I missing something, Can someone guide me through this?

Srinivasan M gravatar imageSrinivasan M ( 2019-07-16 10:26:18 -0500 )edit
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answered 2015-06-08 04:17:13 -0500

abhiguru gravatar image

updated 2015-06-08 04:32:33 -0500

Matches for knnMatch need to be

 std::vector< std::vector< DMatch > > & matches,

and NOT

std::vector< DMatch > &
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This was very useful for me - I was missing the cv::NORM_HAMMING argument, so many thanks indeed!

Mike Pelton gravatar imageMike Pelton ( 2018-11-07 12:00:15 -0500 )edit
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Asked: 2015-04-26 14:48:16 -0500

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Last updated: Jun 08 '15