fixed some mistakes in gpu docs, added docs for gpu stereo.

doc/gpu_features2d.tex

@@ -1,4 +1,5 @@
-\section{Feature detection and description}
+\section{Feature Detection and Description}
+
 
 \cvclass{gpu::SURFParams\_GPU}
 Various SURF algorithm parameters. 
@@ -55,6 +56,7 @@ struct SURFParams_GPU
 };
 \end{lstlisting}
 
+
 \cvclass{gpu::SURF\_GPU}
 Class for extracting Speeded Up Robust Features from an image.
 
@@ -112,10 +114,13 @@ public:
 
 The class \texttt{SURF\_GPU} implements Speeded Up Robust Features descriptor. There is fast multi-scale Hessian keypoint detector that can be used to find the keypoints (which is the default option), but the descriptors can be also computed for the user-specified keypoints. Supports only 8 bit grayscale images.
 
-The class \texttt{SURF\_GPU} can store results to GPU and CPU memory and provides static functions to convert results between CPU and GPU version (\texttt{uploadKeypoints}, \texttt{downloadKeypoints}, \texttt{downloadDescriptors}). CPU results has the same format as \hyperref[cv.class.SURF]{cv::SURF} results. GPU results is stored to \texttt{GpuMat}. \texttt{keypoints} matrix is one row matrix with \texttt{CV\_32FC6} type. It contains 6 float values per feature: \texttt{x, y, size, response, angle, octave}. \texttt{descriptors} matrix is \texttt{nFeatures} x \texttt{descriptorSize} matrix with \texttt{CV\_32FC1} type.
+The class \texttt{SURF\_GPU} can store results to GPU and CPU memory and provides static functions to convert results between CPU and GPU version (\texttt{uploadKeypoints}, \texttt{downloadKeypoints}, \texttt{downloadDescriptors}). CPU results has the same format as \hyperref[cv.class.SURF]{cv::SURF} results. GPU results are stored to \texttt{GpuMat}. \texttt{keypoints} matrix is one row matrix with \texttt{CV\_32FC6} type. It contains 6 float values per feature: \texttt{x, y, size, response, angle, octave}. \texttt{descriptors} matrix is $\texttt{nFeatures} \times \texttt{descriptorSize}$ matrix with \texttt{CV\_32FC1} type.
 
 The class \texttt{SURF\_GPU} uses some buffers and provides access to it. All buffers can be safely released between function calls. 
 
+See also: \hyperref[cv.class.SURF]{cv::SURF}.
+
+
 \cvclass{gpu::BruteForceMatcher\_GPU}
 Brute-force descriptor matcher. For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one. This descriptor matcher supports masking permissible matches between descriptor sets.
 
@@ -203,61 +208,77 @@ private:
 };
 \end{lstlisting}
 
-The class \texttt{BruteForceMatcher\_GPU} has the similar interface with class \hyperref[cv.class.DescriptorMatcher]{cv::DescriptorMatcher}. It has two groups of match methods: for matching descriptors of one image with other image or with image set. Also all functions have alternative: save results to GPU memory or to CPU memory. \texttt{BruteForceMatcher\_GPU} is templated on the distance metric as \hyperref[cv.class.BruteForceMatcher]{cv::BruteForceMatcher}, but supports only \texttt{L1} and \texttt{L2} distance types.
+The class \texttt{BruteForceMatcher\_GPU} has the similar interface to class \hyperref[cv.class.DescriptorMatcher]{cv::DescriptorMatcher}. It has two groups of match methods: for matching descriptors of one image with other image or with image set. Also all functions have alternative: save results to GPU memory or to CPU memory.
+
+\texttt{Distance} template parameter is kept for CPU/GPU interfaces similarity. \texttt{BruteForceMatcher\_GPU} supports only \texttt{L1<float>} and \texttt{L2<float>} distance types.
+
+See also: \hyperref[cv.class.DescriptorMatcher]{cv::DescriptorMatcher}, \hyperref[cv.class.BruteForceMatcher]{cv::BruteForceMatcher}.
+
+
+\cvfunc{cv::gpu::BruteForceMatcher\_GPU::match}\label{cppfunc.gpu.BruteForceMatcher.match}
+Finds the best match for each descriptor from a query set with train descriptors.
 
-\cvfunc{gpu::BruteForceMatcher\_GPU::match}\label{cppfunc.gpu.BruteForceMatcher.match}
-Find the best match for each descriptor from a query set with train descriptors. This function is equivalent of \cvCppCross{DescriptorMatcher::match}.
-\cvdefCpp{
-void match(const GpuMat\& queryDescs, \par const GpuMat\& trainDescs, \par std::vector<DMatch>\& matches, \par const GpuMat\& mask = GpuMat());
-}
 \cvdefCpp{
+void match(const GpuMat\& queryDescs, \par const GpuMat\& trainDescs, \par std::vector<DMatch>\& matches, \par const GpuMat\& mask = GpuMat());\newline
 void match(const GpuMat\& queryDescs, \par std::vector<DMatch>\& matches, \par const std::vector<GpuMat>\& masks = std::vector<GpuMat>());
 }
 
-\cvfunc{gpu::BruteForceMatcher\_GPU::matchSingle}\label{cppfunc.gpu.BruteForceMatcher.matchSingle}
-Find one best match for each query descriptor. Results stored to GPU memory.
+See also: \cvCppCross{DescriptorMatcher::match}.
+
+
+\cvfunc{cv::gpu::BruteForceMatcher\_GPU::matchSingle}\label{cppfunc.gpu.BruteForceMatcher.matchSingle}
+Finds the best match for each query descriptor. Results will be stored to GPU memory.
+
 \cvdefCpp{
 void matchSingle(const GpuMat\& queryDescs, \par const GpuMat\& trainDescs, \par GpuMat\& trainIdx, \par GpuMat\& distance, \par const GpuMat\& mask = GpuMat());
 }
+
 \begin{description}
 \cvarg{queryDescs} {Query set of descriptors.}
 \cvarg{trainDescs} {Train set of descriptors. This will not be added to train descriptors collection stored in class object.}
-\cvarg{trainIdx} {One row \texttt{CV\_32SC1} matrix. Will contain best train index for each query. If some query descriptor masked out in \texttt{mask} it will contain -1.}
-\cvarg{distance} {One row \texttt{CV\_32FC1} matrix. Will contain best distance for each query. If some query descriptor masked out in \texttt{mask} it will contain \texttt{FLT\_MAX}.}
+\cvarg{trainIdx} {One row \texttt{CV\_32SC1} matrix. Will contain the best train index for each query. If some query descriptors masked out in \texttt{mask} it will contain -1.}
+\cvarg{distance} {One row \texttt{CV\_32FC1} matrix. Will contain the best distance for each query. If some query descriptors masked out in \texttt{mask} it will contain \texttt{FLT\_MAX}.}
 \cvarg{mask}{Mask specifying permissible matches between input query and train matrices of descriptors.}
 \end{description}
 
-\cvfunc{gpu::BruteForceMatcher\_GPU::matchCollection}\label{cppfunc.gpu.BruteForceMatcher.matchCollection}
-Find one best match for each query descriptor from train collection. Results stored to GPU memory.
+
+\cvfunc{cv::gpu::BruteForceMatcher\_GPU::matchCollection}\label{cppfunc.gpu.BruteForceMatcher.matchCollection}
+Find the best match for each query descriptor from train collection. Results will be stored to GPU memory.
+
 \cvdefCpp{
 void matchCollection(const GpuMat\& queryDescs, \par const GpuMat\& trainCollection, \par GpuMat\& trainIdx, \par GpuMat\& imgIdx, \par GpuMat\& distance, \par const GpuMat\& maskCollection);
 }
+
 \begin{description}
 \cvarg{queryDescs} {Query set of descriptors.}
-\cvarg{trainCollection} {\texttt{GpuMat} with train collection. It can be obtained from train descriptors collection that was set using \texttt{add} method by \hyperref[cppfunc.gpu.BruteForceMatcher.makeGpuCollection]{makeGpuCollection}. Or it can contain user defined collection. It must be one row matrix, each element is a \texttt{DevMem2D} that points to one train descriptors matrix (matrix must have \texttt{CV\_32FC1} type).}
-\cvarg{trainIdx} {One row \texttt{CV\_32SC1} matrix. Will contain best train index for each query. If some query descriptor masked out in \texttt{mask} it will contain -1.}
-\cvarg{imgIdx} {One row \texttt{CV\_32SC1} matrix. Will contain image train index for each query. If some query descriptor masked out in \texttt{mask} it will contain -1.}
-\cvarg{distance} {One row \texttt{CV\_32FC1} matrix. Will contain best distance for each query. If some query descriptor masked out in \texttt{mask} it will contain \texttt{FLT\_MAX}.}
-\cvarg{maskCollection}{\texttt{GpuMat} with set of masks. It can be obtained from \texttt{std::vector<GpuMat>} by \hyperref[cppfunc.gpu.BruteForceMatcher.makeGpuCollection]{makeGpuCollection}. Or it can contain user defined mask set. It must be empty matrix or one row matrix, each element is a \texttt{PtrStep} that points to one mask (must have \texttt{CV\_8UC1} type).}
+\cvarg{trainCollection} {\texttt{GpuMat} containing train collection. It can be obtained from train descriptors collection that was set using \texttt{add} method by \hyperref[cppfunc.gpu.BruteForceMatcher.makeGpuCollection]{makeGpuCollection}. Or it can contain user defined collection. It must be one row matrix, each element is a \texttt{DevMem2D} that points to one train descriptors matrix.}
+\cvarg{trainIdx} {One row \texttt{CV\_32SC1} matrix. Will contain the best train index for each query. If some query descriptors masked out in \texttt{maskCollection} it will contain -1.}
+\cvarg{imgIdx} {One row \texttt{CV\_32SC1} matrix. Will contain image train index for each query. If some query descriptors masked out in \texttt{maskCollection} it will contain -1.}
+\cvarg{distance} {One row \texttt{CV\_32FC1} matrix. Will contain the best distance for each query. If some query descriptors masked out in \texttt{maskCollection} it will contain \texttt{FLT\_MAX}.}
+\cvarg{maskCollection}{\texttt{GpuMat} containing set of masks. It can be obtained from \texttt{std::vector<GpuMat>} by \hyperref[cppfunc.gpu.BruteForceMatcher.makeGpuCollection]{makeGpuCollection}. Or it can contain user defined mask set. It must be empty matrix or one row matrix, each element is a \texttt{PtrStep} that points to one mask.}
 \end{description}
 
-\cvfunc{gpu::BruteForceMatcher\_GPU::makeGpuCollection}\label{cppfunc.gpu.BruteForceMatcher.makeGpuCollection}
-Make gpu collection of train descriptors and masks in suitable format for \hyperref[cppfunc.gpu.BruteForceMatcher.matchCollection]{matchCollection} function.
+
+\cvfunc{cv::gpu::BruteForceMatcher\_GPU::makeGpuCollection}\label{cppfunc.gpu.BruteForceMatcher.makeGpuCollection}
+Makes gpu collection of train descriptors and masks in suitable format for \hyperref[cppfunc.gpu.BruteForceMatcher.matchCollection]{matchCollection} function.
+
 \cvdefCpp{
 void makeGpuCollection(GpuMat\& trainCollection, \par GpuMat\& maskCollection, \par const vector<GpuMat>\& masks = std::vector<GpuMat>());
 }
 
-\cvfunc{gpu::BruteForceMatcher\_GPU::matchDownload}\label{cppfunc.gpu.BruteForceMatcher.matchDownload}
-Download \texttt{trainIdx}, \texttt{imgIdx} and \texttt{distance} matrices obtained by \hyperref[cppfunc.gpu.BruteForceMatcher.matchSingle]{matchSingle} or \hyperref[cppfunc.gpu.BruteForceMatcher.matchCollection]{matchCollection} to CPU vector with \hyperref[cv.class.DMatch]{cv::DMatch}.
+
+\cvfunc{cv::gpu::BruteForceMatcher\_GPU::matchDownload}\label{cppfunc.gpu.BruteForceMatcher.matchDownload}
+Downloads \texttt{trainIdx}, \texttt{imgIdx} and \texttt{distance} matrices obtained via \hyperref[cppfunc.gpu.BruteForceMatcher.matchSingle]{matchSingle} or \hyperref[cppfunc.gpu.BruteForceMatcher.matchCollection]{matchCollection} to CPU vector with \hyperref[cv.class.DMatch]{cv::DMatch}.
+
 \cvdefCpp{
-static void matchDownload(const GpuMat\& trainIdx, \par const GpuMat\& distance, \par std::vector<DMatch>\& matches);
-}
-\cvdefCpp{
-static void matchDownload(const GpuMat\& trainIdx, \par GpuMat\& imgIdx, \par const GpuMat\& distance, \par std::vector<DMatch>\& matches);
+void matchDownload(const GpuMat\& trainIdx, \par const GpuMat\& distance, \par std::vector<DMatch>\& matches); \newline
+void matchDownload(const GpuMat\& trainIdx, \par GpuMat\& imgIdx, \par const GpuMat\& distance, \par std::vector<DMatch>\& matches);
 }
 
-\cvfunc{gpu::BruteForceMatcher\_GPU::knnMatch}\label{cppfunc.gpu.BruteForceMatcher.knnMatch}
-Find the k best matches for each descriptor from a query set with train descriptors. Found k (or less if not possible) matches are returned in distance increasing order. This function is equivalent of \cvCppCross{DescriptorMatcher::knnMatch}.
+
+\cvfunc{cv::gpu::BruteForceMatcher\_GPU::knnMatch}\label{cppfunc.gpu.BruteForceMatcher.knnMatch}
+Finds the k best matches for each descriptor from a query set with train descriptors. Found k (or less if not possible) matches are returned in distance increasing order.
+
 \cvdefCpp{
 void knnMatch(const GpuMat\& queryDescs, \par const GpuMat\& trainDescs, \par std::vector< std::vector<DMatch> >\& matches, \par int k, \par const GpuMat\& mask = GpuMat(), \par bool compactResult = false);
 }
@@ -265,29 +286,38 @@ void knnMatch(const GpuMat\& queryDescs, \par const GpuMat\& trainDescs, \par st
 void knnMatch(const GpuMat\& queryDescs, \par std::vector< std::vector<DMatch> >\& matches, \par int k, \par const std::vector<GpuMat>\& masks = std::vector<GpuMat>(), \par bool compactResult = false );
 }
 
-\cvfunc{gpu::BruteForceMatcher\_GPU::knnMatch}\label{cppfunc.gpu.BruteForceMatcher.knnMatchSingle}
-Find the k best matches for each descriptor from a query set with train descriptors. Found k (or less if not possible) matches are returned in distance increasing order. Results stored to GPU memory.
+See also: \cvCppCross{DescriptorMatcher::knnMatch}.
+
+
+\cvfunc{cv::gpu::BruteForceMatcher\_GPU::knnMatch}\label{cppfunc.gpu.BruteForceMatcher.knnMatchSingle}
+Finds the k best matches for each descriptor from a query set with train descriptors. Found k (or less if not possible) matches are returned in distance increasing order. Results will be stored to GPU memory.
+
 \cvdefCpp{
 void knnMatch(const GpuMat\& queryDescs, \par const GpuMat\& trainDescs, \par GpuMat\& trainIdx, \par GpuMat\& distance, \par GpuMat\& allDist, \par int k, \par const GpuMat\& mask = GpuMat());
 }
+
 \begin{description}
 \cvarg{queryDescs} {Query set of descriptors.}
 \cvarg{trainDescs} {Train set of descriptors. This will not be added to train descriptors collection stored in class object.}
-\cvarg{trainIdx} {Matrix \texttt{nQueries} x \texttt{k} with type \texttt{CV\_32SC1}. \texttt{trainIdx.at<int>(queryIdx, i)} will contain index of i'th best trains. If some query descriptor masked out in \texttt{mask} it will contain -1.}
-\cvarg{distance} {Matrix \texttt{nQuery} x \texttt{k} with type \texttt{CV\_32FC1}. Will contain distance for each query and i'th best trains. If some query descriptor masked out in \texttt{mask} it will contain \texttt{FLT\_MAX}.}
-\cvarg{allDist} {Buffer to store all distances between query descriptors and train descriptors. It have size \texttt{nQuery} x \texttt{nTrain} and \texttt{CV\_32F} type. \texttt{allDist.at<float>(queryIdx, trainIdx)} will contain \texttt{FLT\_MAX}, if \texttt{trainIdx} is one from k best, otherwise it will contain distance between \texttt{queryIdx} and \texttt{trainIdx} descriptors.}
-\cvarg{k}{Count of best matches will be found per each query descriptor (or less if it's not possible).}
+\cvarg{trainIdx} {Matrix with $\texttt{nQueries} \times \texttt{k}$ size and \texttt{CV\_32SC1} type. \texttt{trainIdx.at<int>(queryIdx, i)} will contain index of the i'th best trains. If some query descriptors masked out in \texttt{mask} it will contain -1.}
+\cvarg{distance} {Matrix with $\texttt{nQuery} \times \texttt{k}$ and \texttt{CV\_32FC1} type. Will contain distance for each query and the i'th best trains. If some query descriptors masked out in \texttt{mask} it will contain \texttt{FLT\_MAX}.}
+\cvarg{allDist} {Buffer to store all distances between query descriptors and train descriptors. It will have $\texttt{nQuery} \times \texttt{nTrain}$ size and \texttt{CV\_32FC1} type. \texttt{allDist.at<float>(queryIdx, trainIdx)} will contain \texttt{FLT\_MAX}, if \texttt{trainIdx} is one from k best, otherwise it will contain distance between \texttt{queryIdx} and \texttt{trainIdx} descriptors.}
+\cvarg{k}{Number of the best matches will be found per each query descriptor (or less if it's not possible).}
 \cvarg{mask}{Mask specifying permissible matches between input query and train matrices of descriptors.}
 \end{description}
 
-\cvfunc{gpu::BruteForceMatcher\_GPU::knnMatchDownload}\label{cppfunc.gpu.BruteForceMatcher.knnMatchDownload}
-Download \texttt{trainIdx} and \texttt{distance} matrices obtained by \hyperref[cppfunc.gpu.BruteForceMatcher.knnMatchSingle]{knnMatch} to CPU vector with \hyperref[cv.class.DMatch]{cv::DMatch}. If \texttt{compactResult} is true \texttt{matches} vector will not contain matches for fully masked out query descriptors.
+
+\cvfunc{cv::gpu::BruteForceMatcher\_GPU::knnMatchDownload}\label{cppfunc.gpu.BruteForceMatcher.knnMatchDownload}
+Downloads \texttt{trainIdx} and \texttt{distance} matrices obtained via \hyperref[cppfunc.gpu.BruteForceMatcher.knnMatchSingle]{knnMatch} to CPU vector with \hyperref[cv.class.DMatch]{cv::DMatch}. If \texttt{compactResult} is true \texttt{matches} vector will not contain matches for fully masked out query descriptors.
+
 \cvdefCpp{
-static void knnMatchDownload(const GpuMat\& trainIdx, \par const GpuMat\& distance, \par std::vector< std::vector<DMatch> >\& matches, \par bool compactResult = false);
+void knnMatchDownload(const GpuMat\& trainIdx, \par const GpuMat\& distance, \par std::vector< std::vector<DMatch> >\& matches, \par bool compactResult = false);
 }
 
-\cvfunc{gpu::BruteForceMatcher\_GPU::radiusMatch}\label{cppfunc.gpu.BruteForceMatcher.radiusMatch}
-Find the best matches for each query descriptor which have distance less than given threshold. Found matches are returned in distance increasing order. This function is equivalent of \cvCppCross{DescriptorMatcher::radiusMatch}. Works only on device with Compute Capability \texttt{>=} 1.1.
+
+\cvfunc{cv::gpu::BruteForceMatcher\_GPU::radiusMatch}\label{cppfunc.gpu.BruteForceMatcher.radiusMatch}
+Finds the best matches for each query descriptor which have distance less than given threshold. Found matches are returned in distance increasing order.
+
 \cvdefCpp{
 void radiusMatch(const GpuMat\& queryDescs, \par const GpuMat\& trainDescs, \par std::vector< std::vector<DMatch> >\& matches, \par float maxDistance, \par const GpuMat\& mask = GpuMat(), \par bool compactResult = false);
 }
@@ -295,23 +325,35 @@ void radiusMatch(const GpuMat\& queryDescs, \par const GpuMat\& trainDescs, \par
 void radiusMatch(const GpuMat\& queryDescs, \par std::vector< std::vector<DMatch> >\& matches, \par float maxDistance, \par const std::vector<GpuMat>\& masks = std::vector<GpuMat>(), \par bool compactResult = false);
 }
 
-\cvfunc{gpu::BruteForceMatcher\_GPU::radiusMatch}\label{cppfunc.gpu.BruteForceMatcher.radiusMatchSingle}
-Find the best matches for each query descriptor which have distance less than given threshold. Results stored to GPU memory. Results are not sorted by distance increasing order. Works only on device with Compute Capability \texttt{>=} 1.1.
+This function works only on devices with Compute Capability $>=$ 1.1.
+
+See also: \cvCppCross{DescriptorMatcher::radiusMatch}.
+
+
+\cvfunc{cv::gpu::BruteForceMatcher\_GPU::radiusMatch}\label{cppfunc.gpu.BruteForceMatcher.radiusMatchSingle}
+Finds the best matches for each query descriptor which have distance less than given threshold. Results will be stored to GPU memory.
+
 \cvdefCpp{
 void radiusMatch(const GpuMat\& queryDescs, \par const GpuMat\& trainDescs, \par GpuMat\& trainIdx, \par GpuMat\& nMatches, \par GpuMat\& distance, \par float maxDistance, \par const GpuMat\& mask = GpuMat());
 }
+
 \begin{description}
 \cvarg{queryDescs} {Query set of descriptors.}
 \cvarg{trainDescs} {Train set of descriptors. This will not be added to train descriptors collection stored in class object.}
-\cvarg{trainIdx} {\texttt{trainIdx.at<int>(queryIdx, i)} will contain i'th train index \newline\texttt{(i < min(nMatches.at<unsigned int>(0, queryIdx), trainIdx.cols)}. If \texttt{trainIdx} is empty, it will be created with size \texttt{nQuery} x \texttt{nTrain}. Or it can be allocated by user (it must have \texttt{nQuery} rows and \texttt{CV\_32SC1} type). Cols can be less than \texttt{nTrain}, but it can be that matcher won't find all matches, because it haven't enough memory to store results.}
+\cvarg{trainIdx} {\texttt{trainIdx.at<int>(queryIdx, i)} will contain i'th train index \newline\texttt{(i < min(nMatches.at<unsigned int>(0, queryIdx), trainIdx.cols)}. If \texttt{trainIdx} is empty, it will be created with size $\texttt{nQuery} \times \texttt{nTrain}$. Or it can be allocated by user (it must have \texttt{nQuery} rows and \texttt{CV\_32SC1} type). Cols can be less than \texttt{nTrain}, but it can be that matcher won't find all matches, because it haven't enough memory to store results.}
 \cvarg{nMatches} {\texttt{nMatches.at<unsigned int>(0, queryIdx)} will contain matches count for \texttt{queryIdx}. Carefully, \texttt{nMatches} can be greater than \texttt{trainIdx.cols} - it means that matcher didn't find all matches, because it didn't have enough memory.}
-\cvarg{distance} {\texttt{distance.at<int>(queryIdx, i)} will contain i'th distance \newline\texttt{(i < min(nMatches.at<unsigned int>(0, queryIdx), trainIdx.cols)}. If \texttt{trainIdx} is empty, it will be created with size \texttt{nQuery} x \texttt{nTrain}. Otherwise it must be also allocated by user (it must have the same size as \texttt{trainIdx} and \texttt{CV\_32FC1} type).}
-\cvarg{maxDistance}{The threshold to found match distances.}
+\cvarg{distance} {\texttt{distance.at<int>(queryIdx, i)} will contain i'th distance \newline\texttt{(i < min(nMatches.at<unsigned int>(0, queryIdx), trainIdx.cols)}. If \texttt{trainIdx} is empty, it will be created with size $\texttt{nQuery} \times \texttt{nTrain}$. Otherwise it must be also allocated by user (it must have the same size as \texttt{trainIdx} and \texttt{CV\_32FC1} type).}
+\cvarg{maxDistance}{Distance threshold.}
 \cvarg{mask}{Mask specifying permissible matches between input query and train matrices of descriptors.}
 \end{description}
 
-\cvfunc{gpu::BruteForceMatcher\_GPU::radiusMatchDownload}\label{cppfunc.gpu.BruteForceMatcher.radiusMatchDownload}
-Download \texttt{trainIdx}, \texttt{nMatches} and \texttt{distance} matrices obtained by \hyperref[cppfunc.gpu.BruteForceMatcher.radiusMatchSingle]{radiusMatch} to CPU vector with \hyperref[cv.class.DMatch]{cv::DMatch}. If \texttt{compactResult} is true \texttt{matches} vector will not contain matches for fully masked out query descriptors.
+In contrast to \hyperref[cppfunc.gpu.BruteForceMatcher.radiusMatch]{cv::gpu::BruteForceMather\_GPU::radiusMatch} results are not sorted by distance increasing order.
+
+This function works only on devices with Compute Capability $>=$ 1.1.
+
+\cvfunc{cv::gpu::BruteForceMatcher\_GPU::radiusMatchDownload}\label{cppfunc.gpu.BruteForceMatcher.radiusMatchDownload}
+Downloads \texttt{trainIdx}, \texttt{nMatches} and \texttt{distance} matrices obtained via \hyperref[cppfunc.gpu.BruteForceMatcher.radiusMatchSingle]{radiusMatch} to CPU vector with \hyperref[cv.class.DMatch]{cv::DMatch}. If \texttt{compactResult} is true \texttt{matches} vector will not contain matches for fully masked out query descriptors.
+
 \cvdefCpp{
-static void radiusMatchDownload(const GpuMat\& trainIdx, \par const GpuMat\& nMatches, \par const GpuMat\& distance, \par std::vector< std::vector<DMatch> >\& matches, \par bool compactResult = false);
+void radiusMatchDownload(const GpuMat\& trainIdx, \par const GpuMat\& nMatches, \par const GpuMat\& distance, \par std::vector< std::vector<DMatch> >\& matches, \par bool compactResult = false);
 }