我有一个应用程序，我正在接收一组图像，我希望在其中监视设定ROI中的检测到的功能。这是使用ORB检测器完成的。在第一张图片中，我使用检测器找到给定ROI的“参考”关键点和描述符。对于后续图像，我找到相同ROI的“测试”关键点和描述符。然后我使用knn matcher来查找引用和测试描述符之间的匹配。最后，我尝试找到“最佳”匹配，将关联的关键点添加到“匹配关键点”集合，然后计算“匹配强度”。此匹配强度旨在指示参考图像中找到的关键点与测试图像中的关键点的匹配程度。
我有几个问题：
1 - 这是功能检测器的有效用途吗？我知道简单的模板匹配可能会给我类似的结果，但我希望避免照明中的细微变化。
2 - 我是否正确筛选了我的比赛以获得“好”的比赛，然后我获得了该比赛正确关联的关键点？
3 - 我的代码似乎按原样工作，但是，如果我尝试使用流转移到OpenCV调用的异步版本，我会得到一个异常： “函数cv :: cuda :: GpuMat :: setTo中的无效资源句柄”，它发生在对ORB_Impl :: buildScalePyramids（从ORB_Impl :: detectAndComputeAsync调用）的调用中。请参阅下面的“NewFrame”函数的异步版本。这只是让我觉得我没有正确设置所有这些。
这是我的代码：
void Matcher::Matcher()
{
// create ORB detector and descriptor matcher
m_b = cuda::ORB::create(500, 1.2f, 8, 31, 0, 2, 0, 31, 20, true);
m_descriptorMatcher = cv::cuda::DescriptorMatcher::createBFMatcher(cv::NORM_HAMMING);
}
void Matcher::Configure(int imageWidth, int imageHeight, int roiX, int roiY, int roiW, int roiH)
{
// set member variables
m_imageWidth = imageWidth;
m_imageHeight = imageHeight;
m_roiX = roiX;
m_roiY = roiY;
m_roiW = roiW;
m_roiH = roiH;
m_GpuRefSet = false; // set flag indicating reference not yet set
// create mask for specified ROI
m_mask = GpuMat(imageHeight,imageWidth, CV_8UC1, Scalar::all(0));
cv::Rect rect = cv::Rect(m_roiX, m_roiY, m_roiW, m_roiH);
m_mask(rect).setTo(Scalar::all(255));
}
double Matcher::NewFrame(void *pImagedata)
{
// pImagedata = pointer to BGRA byte array
// m_imageHeight and m_imageWidth have already been set
// m_b is a pointer to the ORB detector
if (!m_GpuRefSet)
{ // 1st time through (after call to Matcher::Configure), set reference keypoints and descriptors
cv::cuda::GpuMat mat1(m_imageHeight, m_imageWidth, CV_8UC4, pImagedata); // put image data into GpuMat
cv::cuda::cvtColor(mat1, m_refImage, CV_BGRA2GRAY); // convert to grayscale as required by ORB
m_keyRef.clear(); // clear the vector<KeyPoint>, keypoint vector for reference image
m_b->detectAndCompute(m_refImage, m_mask, m_keyRef, m_descRef, false); // detect keypoints and compute descriptors
m_GpuRefSet = true;
}
cv::cuda::GpuMat mat2(m_imageHeight, m_imageWidth, CV_8UC4, pImagedata); // put image data into GpuMat
cv::cuda::cvtColor(mat2, m_testImage, CV_BGRA2GRAY, 0); // convert to grayscale as required by ORB
m_keyTest.clear(); // clear vector<KeyPoint>, keypoint vector for test image
m_b->detectAndCompute(m_testImage, m_mask, m_keyTest, m_descTest, false); // detect keypoints and compute descriptors
double value = 0.0f; // used to store return value ("match intensity")
// calculate best match for each descriptor
if (m_descTest.rows > 0)
{
m_goodKeypoints.clear(); // clear vector of "good" KeyPoints, vector<KeyPoint>
m_descriptorMatcher->knnMatch(m_descTest, m_descRef, m_matches, 2, noArray()); // find matches
// examine all matches, and collect the KeyPoints whose match distance mets given criteria
for (int i = 0; i<m_matches.size(); i++){
if (m_matches[i][0].distance < m_matches[i][1].distance * m_nnr){ // m_nnr = nearest neighbor ratio (typically 0.6 - 0.8)
m_goodKeypoints.push_back(m_keyRef.at(m_matches[i][0].trainIdx)); // not sure if getting the correct keypoint here
}
}
// calculate "match intensity", i.e. percent of the keypoints found in the reference image that are also in the test image
value = ((double)m_goodKeypoints.size()) / ((double)m_keyRef.size());
}
else
{
value = 0.0f;
}
return value;
}
这是NewFrame函数的流/异步版本失败：
double Matcher::NewFrame(void *pImagedata)
{
if (m_b.empty()) return 0.0f;
if (!m_GpuRefSet)
{
try
{
cv::cuda::GpuMat mat1(m_imageHeight, m_imageWidth, CV_8UC4, pImagedata);
cv::cuda::cvtColor(mat1, m_refImage, CV_BGRA2GRAY);
m_keyRef.clear();
m_b->detectAndComputeAsync(m_refImage, m_mask, m_keyRef, m_descRef, false,m_stream); // FAILS HERE
m_stream.waitForCompletion();
m_GpuRefSet = true;
}
catch (Exception e)
{
string msg = e.msg;
}
}
cv::cuda::GpuMat mat2(m_imageHeight, m_imageWidth, CV_8UC4, pImagedata);
cv::cuda::cvtColor(mat2, m_testImage, CV_BGRA2GRAY, 0, m_stream);
m_keyTest.clear();
m_b->detectAndComputeAsync(m_testImage, m_mask, m_keyTest, m_descTest, false, m_stream);
m_stream.waitForCompletion();
double value = 0.0f;
// calculate best match for each descriptor
if (m_descTest.rows > 0)
{
m_goodKeypoints.clear();
m_descriptorMatcher->knnMatchAsync(m_descTest, m_descRef, m_matches, 2, noArray(), m_stream);
m_stream.waitForCompletion();
for (int i = 0; i<m_matches.size(); i++){
if (m_matches[i][0].distance < m_matches[i][1].distance * m_nnr) // m_nnr = nearest neighbor ratio
{
m_goodKeypoints.push_back(m_keyRef.at(m_matches[i][0].trainIdx));
}
}
value = ((double)m_goodKeypoints.size()) / ((double)m_keyRef.size());
}
else
{
value = 0.0f;
}
if (value > 1.0f) value = 1.0f;
return value;
}
任何建议/意见都将不胜感激。
谢谢！
答案 0 :(得分：0)
经过一些试验，我确信这确实是对ORB探测器的合理使用，而且我的测试是为了'＃good;＆＃34;使用最近邻比率方法似乎也有效。这回答了上面的问题＃1和＃2。
与问题＃3相关，我确实做了一些发现，为我彻底清理了一些事情。
首先，事实证明我对cv :: cuda :: Stream和cpu线程不够谨慎。虽然我确信它对许多人来说很明显，并且在OpenCV文档中提到过，但是在特定的cv :: cuda :: Stream上放置的任何东西都应该从同一个cpu线程中完成。不这样做并不一定会创建异常，但会产生未确定的行为，可能包括异常。
其次，对我来说，使用异步版本的detectAndCompute和knnMatch在多线程中更可靠。这个似乎与Async版本使用所有基于GPU的参数这一事实有关，其中非Async版本具有基于CPU的矢量参数。 Async和非Async版本似乎都适用于我编写简单的单线程测试应用程序。但是，我的实际应用程序有其他CUDA内核和CUDA视频解码器在其他线程上运行，因此GPU上拥挤的东西。
无论如何，这是我的如何制作Async函数调用的版本，它为我清理了一切。它演示了ORB检测器和描述符匹配器的Async / Stream版本的使用。传入它的cv :: cuda :: Stream可以是cv :: cuda :: Stream :: NullStream（）或你创建的cv :: cuda :: Stream。请记住在使用它的同一个cpu线程上创建流。
我仍然对改进建议感兴趣，但以下似乎也有效。
orb = cuda::ORB::create(500, 1.2f, 8, 31, 0, 2, 0, 31, 20, true);
matcher = cv::cuda::DescriptorMatcher::createBFMatcher(cv::NORM_HAMMING);
// process 1st image
GpuMat imgGray1; // load this with your grayscale image
GpuMat keys1; // this holds the keys detected
GpuMat desc1; // this holds the descriptors for the detected keypoints
GpuMat mask1; // this holds any mask you may want to use, or can be replace by noArray() in the call below if no mask is needed
vector<KeyPoint> cpuKeys1; // holds keypoints downloaded from gpu
//ADD CODE TO LOAD imgGray1
orb->detectAndComputeAsync(imgGray1, mask1, keys1, desc1, false, m_stream);
stream.waitForCompletion();
orb->convert(keys1, cpuKeys1); // download keys to cpu if needed for anything...like displaying or whatever
// process 2nd image
GpuMat imgGray2; // load this with your grayscale image
GpuMat keys2; // this holds the keys detected
GpuMat desc2; // this holds the descriptors for the detected keypoints
GpuMat mask2; // this holds any mask you may want to use, or can be replace by noArray() in the call below if no mask is needed
vector<KeyPoint> cpuKeys2; // holds keypoints downloaded from gpu
//ADD CODE TO LOAD imgGray2
orb->detectAndComputeAsync(imgGray2, mask2, keys2, desc2, false, m_stream);
stream.waitForCompletion();
orb->convert(keys2, cpuKeys2); // download keys to cpu if needed for anything...like displaying or whatever
if (desc2.rows > 0)
{
vector<vector<DMatch>> cpuKnnMatches;
GpuMat gpuKnnMatches; // holds matches on gpu
matcher->knnMatchAsync(desc2, desc1, gpuKnnMatches, 2, noArray(), *stream); // find matches
stream.waitForCompletion();
matcher->knnMatchConvert(gpuKnnMatches, cpuKnnMatches); // download matches from gpu and put into vector<vector<DMatch>> form on cpu
vector<DMatch> matches; // vector of good matches between tested images
for (std::vector<std::vector<cv::DMatch> >::const_iterator it = cpuKnnMatches.begin(); it != cpuKnnMatches.end(); ++it) {
if (it->size() > 1 && (*it)[0].distance / (*it)[1].distance < m_nnr) { // use Nearest-Neighbor Ratio to determine "good" matches
DMatch m = (*it)[0];
matches.push_back(m); // save good matches here
}
}
}
}