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Commit 8a178da1 authored by Vladislav Vinogradov's avatar Vladislav Vinogradov
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refactor CUDA BFMatcher algorithm: 

use new abstract interface and hidden implementation
parent 764d55b8
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modules/cudafeatures2d/include/opencv2/cudafeatures2d.hpp
View file @ 8a178da1
......@@ -63,170 +63,315 @@ namespace cv { namespace cuda {
//! @addtogroup cudafeatures2d
//! @{
/** @brief 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.
//
// DescriptorMatcher
//
The class BFMatcher_CUDA has an interface similar to the class DescriptorMatcher. It has two groups
of match methods: for matching descriptors of one image with another image or with an image set.
Also, all functions have an alternative to save results either to the GPU memory or to the CPU
memory.
/** @brief Abstract base class for matching keypoint descriptors.
@sa DescriptorMatcher, BFMatcher
It has two groups of match methods: for matching descriptors of an image with another image or with
an image set.
*/
class CV_EXPORTS BFMatcher_CUDA
class CV_EXPORTS DescriptorMatcher : public cv::Algorithm
{
public:
explicit BFMatcher_CUDA(int norm = cv::NORM_L2);
//! Add descriptors to train descriptor collection
void add(const std::vector<GpuMat>& descCollection);
//! Get train descriptors collection
const std::vector<GpuMat>& getTrainDescriptors() const;
//! Clear train descriptors collection
void clear();
//! Return true if there are not train descriptors in collection
bool empty() const;
//! Return true if the matcher supports mask in match methods
bool isMaskSupported() const;
//! Find one best match for each query descriptor
void matchSingle(const GpuMat& query, const GpuMat& train,
GpuMat& trainIdx, GpuMat& distance,
const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
//! Download trainIdx and distance and convert it to CPU vector with DMatch
static void matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches);
//! Convert trainIdx and distance to vector with DMatch
static void matchConvert(const Mat& trainIdx, const Mat& distance, std::vector<DMatch>& matches);
//! Find one best match for each query descriptor
void match(const GpuMat& query, const GpuMat& train, std::vector<DMatch>& matches, const GpuMat& mask = GpuMat());
//! Make gpu collection of trains and masks in suitable format for matchCollection function
void makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection, const std::vector<GpuMat>& masks = std::vector<GpuMat>());
//! Find one best match from train collection for each query descriptor
void matchCollection(const GpuMat& query, const GpuMat& trainCollection,
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
const GpuMat& masks = GpuMat(), Stream& stream = Stream::Null());
//! Download trainIdx, imgIdx and distance and convert it to vector with DMatch
static void matchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, std::vector<DMatch>& matches);
//! Convert trainIdx, imgIdx and distance to vector with DMatch
static void matchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, std::vector<DMatch>& matches);
//! Find one best match from train collection for each query descriptor.
void match(const GpuMat& query, std::vector<DMatch>& matches, const std::vector<GpuMat>& masks = std::vector<GpuMat>());
//! Find k best matches for each query descriptor (in increasing order of distances)
void knnMatchSingle(const GpuMat& query, const GpuMat& train,
GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k,
const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
//! Download trainIdx and distance and convert it to vector with DMatch
//! compactResult is used when mask is not empty. If compactResult is false matches
//! vector will have the same size as queryDescriptors rows. If compactResult is true
//! matches vector will not contain matches for fully masked out query descriptors.
static void knnMatchDownload(const GpuMat& trainIdx, const GpuMat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
//! Convert trainIdx and distance to vector with DMatch
static void knnMatchConvert(const Mat& trainIdx, const Mat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
//! Find k best matches for each query descriptor (in increasing order of distances).
//! compactResult is used when mask is not empty. If compactResult is false matches
//! vector will have the same size as queryDescriptors rows. If compactResult is true
//! matches vector will not contain matches for fully masked out query descriptors.
void knnMatch(const GpuMat& query, const GpuMat& train,
std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask = GpuMat(),
bool compactResult = false);
//! Find k best matches from train collection for each query descriptor (in increasing order of distances)
void knnMatch2Collection(const GpuMat& query, const GpuMat& trainCollection,
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
const GpuMat& maskCollection = GpuMat(), Stream& stream = Stream::Null());
//! Download trainIdx and distance and convert it to vector with DMatch
//! compactResult is used when mask is not empty. If compactResult is false matches
//! vector will have the same size as queryDescriptors rows. If compactResult is true
//! matches vector will not contain matches for fully masked out query descriptors.
//! @see BFMatcher_CUDA::knnMatchDownload
static void knnMatch2Download(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
//! Convert trainIdx and distance to vector with DMatch
//! @see BFMatcher_CUDA::knnMatchConvert
static void knnMatch2Convert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
//! Find k best matches for each query descriptor (in increasing order of distances).
//! compactResult is used when mask is not empty. If compactResult is false matches
//! vector will have the same size as queryDescriptors rows. If compactResult is true
//! matches vector will not contain matches for fully masked out query descriptors.
void knnMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, int k,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
//! Find best matches for each query descriptor which have distance less than maxDistance.
//! nMatches.at<int>(0, queryIdx) will contain matches count for queryIdx.
//! carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches,
//! because it didn't have enough memory.
//! If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nTrain / 100), 10),
//! otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
//! Matches doesn't sorted.
void radiusMatchSingle(const GpuMat& query, const GpuMat& train,
GpuMat& trainIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
//! Download trainIdx, nMatches and distance and convert it to vector with DMatch.
//! matches will be sorted in increasing order of distances.
//! compactResult is used when mask is not empty. If compactResult is false matches
//! vector will have the same size as queryDescriptors rows. If compactResult is true
//! matches vector will not contain matches for fully masked out query descriptors.
static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, const GpuMat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
//! Convert trainIdx, nMatches and distance to vector with DMatch.
static void radiusMatchConvert(const Mat& trainIdx, const Mat& distance, const Mat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
//! Find best matches for each query descriptor which have distance less than maxDistance
//! in increasing order of distances).
void radiusMatch(const GpuMat& query, const GpuMat& train,
std::vector< std::vector<DMatch> >& matches, float maxDistance,
const GpuMat& mask = GpuMat(), bool compactResult = false);
//! Find best matches for each query descriptor which have distance less than maxDistance.
//! If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nQuery / 100), 10),
//! otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
//! Matches doesn't sorted.
void radiusMatchCollection(const GpuMat& query, GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), Stream& stream = Stream::Null());
//! Download trainIdx, imgIdx, nMatches and distance and convert it to vector with DMatch.
//! matches will be sorted in increasing order of distances.
//! compactResult is used when mask is not empty. If compactResult is false matches
//! vector will have the same size as queryDescriptors rows. If compactResult is true
//! matches vector will not contain matches for fully masked out query descriptors.
static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, const GpuMat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
//! Convert trainIdx, nMatches and distance to vector with DMatch.
static void radiusMatchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, const Mat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
//! Find best matches from train collection for each query descriptor which have distance less than
//! maxDistance (in increasing order of distances).
void radiusMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, float maxDistance,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
int norm;
private:
std::vector<GpuMat> trainDescCollection;
//
// Factories
//
/** @brief 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 of descriptor
sets.
@param normType One of NORM_L1, NORM_L2, NORM_HAMMING. L1 and L2 norms are
preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and
BRIEF).
*/
static Ptr<DescriptorMatcher> createBFMatcher(int norm = cv::NORM_L2);
//
// Utility
//
/** @brief Returns true if the descriptor matcher supports masking permissible matches.
*/
virtual bool isMaskSupported() const = 0;
//
// Descriptor collection
//
/** @brief Adds descriptors to train a descriptor collection.
If the collection is not empty, the new descriptors are added to existing train descriptors.
@param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same
train image.
*/
virtual void add(const std::vector<GpuMat>& descriptors) = 0;
/** @brief Returns a constant link to the train descriptor collection.
*/
virtual const std::vector<GpuMat>& getTrainDescriptors() const = 0;
/** @brief Clears the train descriptor collection.
*/
virtual void clear() = 0;
/** @brief Returns true if there are no train descriptors in the collection.
*/
virtual bool empty() const = 0;
/** @brief Trains a descriptor matcher.
Trains a descriptor matcher (for example, the flann index). In all methods to match, the method
train() is run every time before matching.
*/
virtual void train() = 0;
//
// 1 to 1 match
//
/** @brief Finds the best match for each descriptor from a query set (blocking version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches. If a query descriptor is masked out in mask , no match is added for this
descriptor. So, matches size may be smaller than the query descriptors count.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
In the first variant of this method, the train descriptors are passed as an input argument. In the
second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is
used. Optional mask (or masks) can be passed to specify which query and training descriptors can be
matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if
mask.at\<uchar\>(i,j) is non-zero.
*/
virtual void match(InputArray queryDescriptors, InputArray trainDescriptors,
std::vector<DMatch>& matches,
InputArray mask = noArray()) = 0;
/** @overload
*/
virtual void match(InputArray queryDescriptors,
std::vector<DMatch>& matches,
const std::vector<GpuMat>& masks = std::vector<GpuMat>()) = 0;
/** @brief Finds the best match for each descriptor from a query set (asynchronous version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches array stored in GPU memory. Internal representation is not defined.
Use DescriptorMatcher::matchConvert method to retrieve results in standard representation.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param stream CUDA stream.
In the first variant of this method, the train descriptors are passed as an input argument. In the
second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is
used. Optional mask (or masks) can be passed to specify which query and training descriptors can be
matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if
mask.at\<uchar\>(i,j) is non-zero.
*/
virtual void matchAsync(InputArray queryDescriptors, InputArray trainDescriptors,
OutputArray matches,
InputArray mask = noArray(),
Stream& stream = Stream::Null()) = 0;
/** @overload
*/
virtual void matchAsync(InputArray queryDescriptors,
OutputArray matches,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
Stream& stream = Stream::Null()) = 0;
/** @brief Converts matches array from internal representation to standard matches vector.
The method is supposed to be used with DescriptorMatcher::matchAsync to get final result.
Call this method only after DescriptorMatcher::matchAsync is completed (ie. after synchronization).
@param gpu_matches Matches, returned from DescriptorMatcher::matchAsync.
@param matches Vector of DMatch objects.
*/
virtual void matchConvert(InputArray gpu_matches,
std::vector<DMatch>& matches) = 0;
//
// knn match
//
/** @brief Finds the k best matches for each descriptor from a query set (blocking version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
@param k Count of best matches found per each query descriptor or less if a query descriptor has
less than k possible matches in total.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
These extended variants of DescriptorMatcher::match methods find several best matches for each query
descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match
for the details about query and train descriptors.
*/
virtual void knnMatch(InputArray queryDescriptors, InputArray trainDescriptors,
std::vector<std::vector<DMatch> >& matches,
int k,
InputArray mask = noArray(),
bool compactResult = false) = 0;
/** @overload
*/
virtual void knnMatch(InputArray queryDescriptors,
std::vector<std::vector<DMatch> >& matches,
int k,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
bool compactResult = false) = 0;
/** @brief Finds the k best matches for each descriptor from a query set (asynchronous version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches array stored in GPU memory. Internal representation is not defined.
Use DescriptorMatcher::knnMatchConvert method to retrieve results in standard representation.
@param k Count of best matches found per each query descriptor or less if a query descriptor has
less than k possible matches in total.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
@param stream CUDA stream.
These extended variants of DescriptorMatcher::matchAsync methods find several best matches for each query
descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::matchAsync
for the details about query and train descriptors.
*/
virtual void knnMatchAsync(InputArray queryDescriptors, InputArray trainDescriptors,
OutputArray matches,
int k,
InputArray mask = noArray(),
Stream& stream = Stream::Null()) = 0;
/** @overload
*/
virtual void knnMatchAsync(InputArray queryDescriptors,
OutputArray matches,
int k,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
Stream& stream = Stream::Null()) = 0;
/** @brief Converts matches array from internal representation to standard matches vector.
The method is supposed to be used with DescriptorMatcher::knnMatchAsync to get final result.
Call this method only after DescriptorMatcher::knnMatchAsync is completed (ie. after synchronization).
@param gpu_matches Matches, returned from DescriptorMatcher::knnMatchAsync.
@param matches Vector of DMatch objects.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
*/
virtual void knnMatchConvert(InputArray gpu_matches,
std::vector< std::vector<DMatch> >& matches,
bool compactResult = false) = 0;
//
// radius match
//
/** @brief For each query descriptor, finds the training descriptors not farther than the specified distance (blocking version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Found matches.
@param maxDistance Threshold for the distance between matched descriptors. Distance means here
metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
in Pixels)!
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
For each query descriptor, the methods find such training descriptors that the distance between the
query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are
returned in the distance increasing order.
*/
virtual void radiusMatch(InputArray queryDescriptors, InputArray trainDescriptors,
std::vector<std::vector<DMatch> >& matches,
float maxDistance,
InputArray mask = noArray(),
bool compactResult = false) = 0;
/** @overload
*/
virtual void radiusMatch(InputArray queryDescriptors,
std::vector<std::vector<DMatch> >& matches,
float maxDistance,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
bool compactResult = false) = 0;
/** @brief For each query descriptor, finds the training descriptors not farther than the specified distance (asynchronous version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches array stored in GPU memory. Internal representation is not defined.
Use DescriptorMatcher::radiusMatchConvert method to retrieve results in standard representation.
@param maxDistance Threshold for the distance between matched descriptors. Distance means here
metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
in Pixels)!
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
@param stream CUDA stream.
For each query descriptor, the methods find such training descriptors that the distance between the
query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are
returned in the distance increasing order.
*/
virtual void radiusMatchAsync(InputArray queryDescriptors, InputArray trainDescriptors,
OutputArray matches,
float maxDistance,
InputArray mask = noArray(),
Stream& stream = Stream::Null()) = 0;
/** @overload
*/
virtual void radiusMatchAsync(InputArray queryDescriptors,
OutputArray matches,
float maxDistance,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
Stream& stream = Stream::Null()) = 0;
/** @brief Converts matches array from internal representation to standard matches vector.
The method is supposed to be used with DescriptorMatcher::radiusMatchAsync to get final result.
Call this method only after DescriptorMatcher::radiusMatchAsync is completed (ie. after synchronization).
@param gpu_matches Matches, returned from DescriptorMatcher::radiusMatchAsync.
@param matches Vector of DMatch objects.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
*/
virtual void radiusMatchConvert(InputArray gpu_matches,
std::vector< std::vector<DMatch> >& matches,
bool compactResult = false) = 0;
};
//
......
modules/cudafeatures2d/perf/perf_features2d.cpp
View file @ 8a178da1
......@@ -167,16 +167,16 @@ PERF_TEST_P(DescSize_Norm, BFMatch,
if (PERF_RUN_CUDA())
{
cv::cuda::BFMatcher_CUDA d_matcher(normType);
cv::Ptr<cv::cuda::DescriptorMatcher> d_matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normType);
const cv::cuda::GpuMat d_query(query);
const cv::cuda::GpuMat d_train(train);
cv::cuda::GpuMat d_trainIdx, d_distance;
cv::cuda::GpuMat d_matches;
TEST_CYCLE() d_matcher.matchSingle(d_query, d_train, d_trainIdx, d_distance);
TEST_CYCLE() d_matcher->matchAsync(d_query, d_train, d_matches);
std::vector<cv::DMatch> gpu_matches;
d_matcher.matchDownload(d_trainIdx, d_distance, gpu_matches);
d_matcher->matchConvert(d_matches, gpu_matches);
SANITY_CHECK_MATCHES(gpu_matches);
}
......@@ -226,16 +226,16 @@ PERF_TEST_P(DescSize_K_Norm, BFKnnMatch,
if (PERF_RUN_CUDA())
{
cv::cuda::BFMatcher_CUDA d_matcher(normType);
cv::Ptr<cv::cuda::DescriptorMatcher> d_matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normType);
const cv::cuda::GpuMat d_query(query);
const cv::cuda::GpuMat d_train(train);
cv::cuda::GpuMat d_trainIdx, d_distance, d_allDist;
cv::cuda::GpuMat d_matches;
TEST_CYCLE() d_matcher.knnMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_allDist, k);
TEST_CYCLE() d_matcher->knnMatchAsync(d_query, d_train, d_matches, k);
std::vector< std::vector<cv::DMatch> > matchesTbl;
d_matcher.knnMatchDownload(d_trainIdx, d_distance, matchesTbl);
d_matcher->knnMatchConvert(d_matches, matchesTbl);
std::vector<cv::DMatch> gpu_matches;
toOneRowMatches(matchesTbl, gpu_matches);
......@@ -280,16 +280,16 @@ PERF_TEST_P(DescSize_Norm, BFRadiusMatch,
if (PERF_RUN_CUDA())
{
cv::cuda::BFMatcher_CUDA d_matcher(normType);
cv::Ptr<cv::cuda::DescriptorMatcher> d_matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normType);
const cv::cuda::GpuMat d_query(query);
const cv::cuda::GpuMat d_train(train);
cv::cuda::GpuMat d_trainIdx, d_nMatches, d_distance;
cv::cuda::GpuMat d_matches;
TEST_CYCLE() d_matcher.radiusMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_nMatches, maxDistance);
TEST_CYCLE() d_matcher->radiusMatchAsync(d_query, d_train, d_matches, maxDistance);
std::vector< std::vector<cv::DMatch> > matchesTbl;
d_matcher.radiusMatchDownload(d_trainIdx, d_distance, d_nMatches, matchesTbl);
d_matcher->radiusMatchConvert(d_matches, matchesTbl);
std::vector<cv::DMatch> gpu_matches;
toOneRowMatches(matchesTbl, gpu_matches);
......
modules/cudafeatures2d/src/brute_force_matcher.cpp
View file @ 8a178da1
......@@ -47,37 +47,7 @@ using namespace cv::cuda;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
cv::cuda::BFMatcher_CUDA::BFMatcher_CUDA(int) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::add(const std::vector<GpuMat>&) { throw_no_cuda(); }
const std::vector<GpuMat>& cv::cuda::BFMatcher_CUDA::getTrainDescriptors() const { throw_no_cuda(); return trainDescCollection; }
void cv::cuda::BFMatcher_CUDA::clear() { throw_no_cuda(); }
bool cv::cuda::BFMatcher_CUDA::empty() const { throw_no_cuda(); return true; }
bool cv::cuda::BFMatcher_CUDA::isMaskSupported() const { throw_no_cuda(); return true; }
void cv::cuda::BFMatcher_CUDA::matchSingle(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, const GpuMat&, Stream&) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::matchDownload(const GpuMat&, const GpuMat&, std::vector<DMatch>&) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::matchConvert(const Mat&, const Mat&, std::vector<DMatch>&) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::match(const GpuMat&, const GpuMat&, std::vector<DMatch>&, const GpuMat&) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::makeGpuCollection(GpuMat&, GpuMat&, const std::vector<GpuMat>&) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::matchCollection(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, GpuMat&, const GpuMat&, Stream&) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::matchDownload(const GpuMat&, const GpuMat&, const GpuMat&, std::vector<DMatch>&) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::matchConvert(const Mat&, const Mat&, const Mat&, std::vector<DMatch>&) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::match(const GpuMat&, std::vector<DMatch>&, const std::vector<GpuMat>&) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::knnMatchSingle(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, GpuMat&, int, const GpuMat&, Stream&) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::knnMatchDownload(const GpuMat&, const GpuMat&, std::vector< std::vector<DMatch> >&, bool) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::knnMatchConvert(const Mat&, const Mat&, std::vector< std::vector<DMatch> >&, bool) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::knnMatch(const GpuMat&, const GpuMat&, std::vector< std::vector<DMatch> >&, int, const GpuMat&, bool) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::knnMatch2Collection(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, GpuMat&, const GpuMat&, Stream&) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::knnMatch2Download(const GpuMat&, const GpuMat&, const GpuMat&, std::vector< std::vector<DMatch> >&, bool) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::knnMatch2Convert(const Mat&, const Mat&, const Mat&, std::vector< std::vector<DMatch> >&, bool) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::knnMatch(const GpuMat&, std::vector< std::vector<DMatch> >&, int, const std::vector<GpuMat>&, bool) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::radiusMatchSingle(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, GpuMat&, float, const GpuMat&, Stream&) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::radiusMatchDownload(const GpuMat&, const GpuMat&, const GpuMat&, std::vector< std::vector<DMatch> >&, bool) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::radiusMatchConvert(const Mat&, const Mat&, const Mat&, std::vector< std::vector<DMatch> >&, bool) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::radiusMatch(const GpuMat&, const GpuMat&, std::vector< std::vector<DMatch> >&, float, const GpuMat&, bool) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::radiusMatchCollection(const GpuMat&, GpuMat&, GpuMat&, GpuMat&, GpuMat&, float, const std::vector<GpuMat>&, Stream&) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::radiusMatchDownload(const GpuMat&, const GpuMat&, const GpuMat&, const GpuMat&, std::vector< std::vector<DMatch> >&, bool) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::radiusMatchConvert(const Mat&, const Mat&, const Mat&, const Mat&, std::vector< std::vector<DMatch> >&, bool) { throw_no_cuda(); }
void cv::cuda::BFMatcher_CUDA::radiusMatch(const GpuMat&, std::vector< std::vector<DMatch> >&, float, const std::vector<GpuMat>&, bool) { throw_no_cuda(); }
Ptr<cv::cuda::DescriptorMatcher> cv::cuda::DescriptorMatcher::createBFMatcher(int) { throw_no_cuda(); return Ptr<cv::cuda::DescriptorMatcher>(); }
#else /* !defined (HAVE_CUDA) */
......@@ -155,857 +125,953 @@ namespace cv { namespace cuda { namespace device
}
}}}
////////////////////////////////////////////////////////////////////
// Train collection
cv::cuda::BFMatcher_CUDA::BFMatcher_CUDA(int norm_) : norm(norm_)
namespace
{
}
static void makeGpuCollection(const std::vector<GpuMat>& trainDescCollection,
const std::vector<GpuMat>& masks,
GpuMat& trainCollection,
GpuMat& maskCollection)
{
if (trainDescCollection.empty())
return;
void cv::cuda::BFMatcher_CUDA::add(const std::vector<GpuMat>& descCollection)
{
trainDescCollection.insert(trainDescCollection.end(), descCollection.begin(), descCollection.end());
}
if (masks.empty())
{
Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(PtrStepSzb)));
const std::vector<GpuMat>& cv::cuda::BFMatcher_CUDA::getTrainDescriptors() const
{
return trainDescCollection;
}
PtrStepSzb* trainCollectionCPU_ptr = trainCollectionCPU.ptr<PtrStepSzb>();
void cv::cuda::BFMatcher_CUDA::clear()
{
trainDescCollection.clear();
}
for (size_t i = 0, size = trainDescCollection.size(); i < size; ++i, ++trainCollectionCPU_ptr)
*trainCollectionCPU_ptr = trainDescCollection[i];
bool cv::cuda::BFMatcher_CUDA::empty() const
{
return trainDescCollection.empty();
}
trainCollection.upload(trainCollectionCPU);
maskCollection.release();
}
else
{
CV_Assert( masks.size() == trainDescCollection.size() );
bool cv::cuda::BFMatcher_CUDA::isMaskSupported() const
{
return true;
}
Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(PtrStepSzb)));
Mat maskCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(PtrStepb)));
////////////////////////////////////////////////////////////////////
// Match
PtrStepSzb* trainCollectionCPU_ptr = trainCollectionCPU.ptr<PtrStepSzb>();
PtrStepb* maskCollectionCPU_ptr = maskCollectionCPU.ptr<PtrStepb>();
void cv::cuda::BFMatcher_CUDA::matchSingle(const GpuMat& query, const GpuMat& train,
GpuMat& trainIdx, GpuMat& distance,
const GpuMat& mask, Stream& stream)
{
if (query.empty() || train.empty())
return;
for (size_t i = 0, size = trainDescCollection.size(); i < size; ++i, ++trainCollectionCPU_ptr, ++maskCollectionCPU_ptr)
{
const GpuMat& train = trainDescCollection[i];
const GpuMat& mask = masks[i];
using namespace cv::cuda::device::bf_match;
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.cols == train.rows) );
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& train, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance,
cudaStream_t stream);
*trainCollectionCPU_ptr = train;
*maskCollectionCPU_ptr = mask;
}
static const caller_t callersL1[] =
{
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
matchL1_gpu<int>, matchL1_gpu<float>
};
static const caller_t callersL2[] =
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
};
trainCollection.upload(trainCollectionCPU);
maskCollection.upload(maskCollectionCPU);
}
}
static const caller_t callersHamming[] =
class BFMatcher_Impl : public cv::cuda::DescriptorMatcher
{
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
};
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(train.cols == query.cols && train.type() == query.type());
CV_Assert(norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING);
const caller_t* callers = norm == NORM_L1 ? callersL1 : norm == NORM_L2 ? callersL2 : callersHamming;
const int nQuery = query.rows;
ensureSizeIsEnough(1, nQuery, CV_32S, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32F, distance);
caller_t func = callers[query.depth()];
CV_Assert(func != 0);
public:
explicit BFMatcher_Impl(int norm) : norm_(norm)
{
CV_Assert( norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING );
}
func(query, train, mask, trainIdx, distance, StreamAccessor::getStream(stream));
}
virtual bool isMaskSupported() const { return true; }
void cv::cuda::BFMatcher_CUDA::matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches)
{
if (trainIdx.empty() || distance.empty())
return;
virtual void add(const std::vector<GpuMat>& descriptors)
{
trainDescCollection_.insert(trainDescCollection_.end(), descriptors.begin(), descriptors.end());
}
Mat trainIdxCPU(trainIdx);
Mat distanceCPU(distance);
virtual const std::vector<GpuMat>& getTrainDescriptors() const
{
return trainDescCollection_;
}
matchConvert(trainIdxCPU, distanceCPU, matches);
}
virtual void clear()
{
trainDescCollection_.clear();
}
void cv::cuda::BFMatcher_CUDA::matchConvert(const Mat& trainIdx, const Mat& distance, std::vector<DMatch>& matches)
{
if (trainIdx.empty() || distance.empty())
return;
virtual bool empty() const
{
return trainDescCollection_.empty();
}
CV_Assert(trainIdx.type() == CV_32SC1);
CV_Assert(distance.type() == CV_32FC1 && distance.cols == trainIdx.cols);
virtual void train()
{
}
const int nQuery = trainIdx.cols;
virtual void match(InputArray queryDescriptors, InputArray trainDescriptors,
std::vector<DMatch>& matches,
InputArray mask = noArray());
virtual void match(InputArray queryDescriptors,
std::vector<DMatch>& matches,
const std::vector<GpuMat>& masks = std::vector<GpuMat>());
virtual void matchAsync(InputArray queryDescriptors, InputArray trainDescriptors,
OutputArray matches,
InputArray mask = noArray(),
Stream& stream = Stream::Null());
virtual void matchAsync(InputArray queryDescriptors,
OutputArray matches,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
Stream& stream = Stream::Null());
virtual void matchConvert(InputArray gpu_matches,
std::vector<DMatch>& matches);
virtual void knnMatch(InputArray queryDescriptors, InputArray trainDescriptors,
std::vector<std::vector<DMatch> >& matches,
int k,
InputArray mask = noArray(),
bool compactResult = false);
virtual void knnMatch(InputArray queryDescriptors,
std::vector<std::vector<DMatch> >& matches,
int k,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
bool compactResult = false);
virtual void knnMatchAsync(InputArray queryDescriptors, InputArray trainDescriptors,
OutputArray matches,
int k,
InputArray mask = noArray(),
Stream& stream = Stream::Null());
virtual void knnMatchAsync(InputArray queryDescriptors,
OutputArray matches,
int k,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
Stream& stream = Stream::Null());
virtual void knnMatchConvert(InputArray gpu_matches,
std::vector< std::vector<DMatch> >& matches,
bool compactResult = false);
virtual void radiusMatch(InputArray queryDescriptors, InputArray trainDescriptors,
std::vector<std::vector<DMatch> >& matches,
float maxDistance,
InputArray mask = noArray(),
bool compactResult = false);
virtual void radiusMatch(InputArray queryDescriptors,
std::vector<std::vector<DMatch> >& matches,
float maxDistance,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
bool compactResult = false);
virtual void radiusMatchAsync(InputArray queryDescriptors, InputArray trainDescriptors,
OutputArray matches,
float maxDistance,
InputArray mask = noArray(),
Stream& stream = Stream::Null());
virtual void radiusMatchAsync(InputArray queryDescriptors,
OutputArray matches,
float maxDistance,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
Stream& stream = Stream::Null());
virtual void radiusMatchConvert(InputArray gpu_matches,
std::vector< std::vector<DMatch> >& matches,
bool compactResult = false);
private:
int norm_;
std::vector<GpuMat> trainDescCollection_;
};
matches.clear();
matches.reserve(nQuery);
//
// 1 to 1 match
//
const int* trainIdx_ptr = trainIdx.ptr<int>();
const float* distance_ptr = distance.ptr<float>();
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx, ++trainIdx_ptr, ++distance_ptr)
void BFMatcher_Impl::match(InputArray _queryDescriptors, InputArray _trainDescriptors,
std::vector<DMatch>& matches,
InputArray _mask)
{
int train_idx = *trainIdx_ptr;
if (train_idx == -1)
continue;
float distance_local = *distance_ptr;
DMatch m(queryIdx, train_idx, 0, distance_local);
matches.push_back(m);
GpuMat d_matches;
matchAsync(_queryDescriptors, _trainDescriptors, d_matches, _mask);
matchConvert(d_matches, matches);
}
}
void cv::cuda::BFMatcher_CUDA::match(const GpuMat& query, const GpuMat& train,
std::vector<DMatch>& matches, const GpuMat& mask)
{
GpuMat trainIdx, distance;
matchSingle(query, train, trainIdx, distance, mask);
matchDownload(trainIdx, distance, matches);
}
void cv::cuda::BFMatcher_CUDA::makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection,
const std::vector<GpuMat>& masks)
{
if (empty())
return;
if (masks.empty())
void BFMatcher_Impl::match(InputArray _queryDescriptors,
std::vector<DMatch>& matches,
const std::vector<GpuMat>& masks)
{
Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(PtrStepSzb)));
GpuMat d_matches;
matchAsync(_queryDescriptors, d_matches, masks);
matchConvert(d_matches, matches);
}
PtrStepSzb* trainCollectionCPU_ptr = trainCollectionCPU.ptr<PtrStepSzb>();
void BFMatcher_Impl::matchAsync(InputArray _queryDescriptors, InputArray _trainDescriptors,
OutputArray _matches,
InputArray _mask,
Stream& stream)
{
using namespace cv::cuda::device::bf_match;
for (size_t i = 0, size = trainDescCollection.size(); i < size; ++i, ++trainCollectionCPU_ptr)
*trainCollectionCPU_ptr = trainDescCollection[i];
const GpuMat query = _queryDescriptors.getGpuMat();
const GpuMat train = _trainDescriptors.getGpuMat();
const GpuMat mask = _mask.getGpuMat();
trainCollection.upload(trainCollectionCPU);
maskCollection.release();
}
else
{
CV_Assert(masks.size() == trainDescCollection.size());
if (query.empty() || train.empty())
{
_matches.release();
return;
}
Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(PtrStepSzb)));
Mat maskCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(PtrStepb)));
CV_Assert( query.channels() == 1 && query.depth() < CV_64F );
CV_Assert( train.cols == query.cols && train.type() == query.type() );
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.rows == query.rows && mask.cols == train.rows) );
PtrStepSzb* trainCollectionCPU_ptr = trainCollectionCPU.ptr<PtrStepSzb>();
PtrStepb* maskCollectionCPU_ptr = maskCollectionCPU.ptr<PtrStepb>();
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& train, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance,
cudaStream_t stream);
for (size_t i = 0, size = trainDescCollection.size(); i < size; ++i, ++trainCollectionCPU_ptr, ++maskCollectionCPU_ptr)
static const caller_t callersL1[] =
{
const GpuMat& train = trainDescCollection[i];
const GpuMat& mask = masks[i];
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
matchL1_gpu<int>, matchL1_gpu<float>
};
static const caller_t callersL2[] =
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
};
static const caller_t callersHamming[] =
{
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
};
CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.cols == train.rows));
const caller_t* callers = norm_ == NORM_L1 ? callersL1 : norm_ == NORM_L2 ? callersL2 : callersHamming;
*trainCollectionCPU_ptr = train;
*maskCollectionCPU_ptr = mask;
const caller_t func = callers[query.depth()];
if (func == 0)
{
CV_Error(Error::StsUnsupportedFormat, "unsupported combination of query.depth() and norm");
}
trainCollection.upload(trainCollectionCPU);
maskCollection.upload(maskCollectionCPU);
}
}
const int nQuery = query.rows;
void cv::cuda::BFMatcher_CUDA::matchCollection(const GpuMat& query, const GpuMat& trainCollection,
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
const GpuMat& masks, Stream& stream)
{
if (query.empty() || trainCollection.empty())
return;
_matches.create(2, nQuery, CV_32SC1);
GpuMat matches = _matches.getGpuMat();
using namespace cv::cuda::device::bf_match;
GpuMat trainIdx(1, nQuery, CV_32SC1, matches.ptr(0));
GpuMat distance(1, nQuery, CV_32FC1, matches.ptr(1));
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance,
cudaStream_t stream);
func(query, train, mask, trainIdx, distance, StreamAccessor::getStream(stream));
}
static const caller_t callersL1[] =
{
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
matchL1_gpu<int>, matchL1_gpu<float>
};
static const caller_t callersL2[] =
void BFMatcher_Impl::matchAsync(InputArray _queryDescriptors,
OutputArray _matches,
const std::vector<GpuMat>& masks,
Stream& stream)
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
};
static const caller_t callersHamming[] =
{
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
};
using namespace cv::cuda::device::bf_match;
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING);
const GpuMat query = _queryDescriptors.getGpuMat();
const caller_t* callers = norm == NORM_L1 ? callersL1 : norm == NORM_L2 ? callersL2 : callersHamming;
const int nQuery = query.rows;
if (query.empty() || trainDescCollection_.empty())
{
_matches.release();
return;
}
ensureSizeIsEnough(1, nQuery, CV_32S, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32S, imgIdx);
ensureSizeIsEnough(1, nQuery, CV_32F, distance);
CV_Assert( query.channels() == 1 && query.depth() < CV_64F );
caller_t func = callers[query.depth()];
CV_Assert(func != 0);
GpuMat trainCollection, maskCollection;
makeGpuCollection(trainDescCollection_, masks, trainCollection, maskCollection);
func(query, trainCollection, masks, trainIdx, imgIdx, distance, StreamAccessor::getStream(stream));
}
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance,
cudaStream_t stream);
void cv::cuda::BFMatcher_CUDA::matchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, std::vector<DMatch>& matches)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
return;
static const caller_t callersL1[] =
{
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
matchL1_gpu<int>, matchL1_gpu<float>
};
static const caller_t callersL2[] =
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
};
static const caller_t callersHamming[] =
{
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
};
Mat trainIdxCPU(trainIdx);
Mat imgIdxCPU(imgIdx);
Mat distanceCPU(distance);
const caller_t* callers = norm_ == NORM_L1 ? callersL1 : norm_ == NORM_L2 ? callersL2 : callersHamming;
matchConvert(trainIdxCPU, imgIdxCPU, distanceCPU, matches);
}
const caller_t func = callers[query.depth()];
if (func == 0)
{
CV_Error(Error::StsUnsupportedFormat, "unsupported combination of query.depth() and norm");
}
void cv::cuda::BFMatcher_CUDA::matchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, std::vector<DMatch>& matches)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
return;
const int nQuery = query.rows;
CV_Assert(trainIdx.type() == CV_32SC1);
CV_Assert(imgIdx.type() == CV_32SC1 && imgIdx.cols == trainIdx.cols);
CV_Assert(distance.type() == CV_32FC1 && distance.cols == trainIdx.cols);
_matches.create(3, nQuery, CV_32SC1);
GpuMat matches = _matches.getGpuMat();
const int nQuery = trainIdx.cols;
GpuMat trainIdx(1, nQuery, CV_32SC1, matches.ptr(0));
GpuMat imgIdx(1, nQuery, CV_32SC1, matches.ptr(1));
GpuMat distance(1, nQuery, CV_32FC1, matches.ptr(2));
matches.clear();
matches.reserve(nQuery);
func(query, trainCollection, maskCollection, trainIdx, imgIdx, distance, StreamAccessor::getStream(stream));
}
const int* trainIdx_ptr = trainIdx.ptr<int>();
const int* imgIdx_ptr = imgIdx.ptr<int>();
const float* distance_ptr = distance.ptr<float>();
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
void BFMatcher_Impl::matchConvert(InputArray _gpu_matches,
std::vector<DMatch>& matches)
{
int _trainIdx = *trainIdx_ptr;
if (_trainIdx == -1)
continue;
int _imgIdx = *imgIdx_ptr;
Mat gpu_matches;
if (_gpu_matches.kind() == _InputArray::CUDA_GPU_MAT)
{
_gpu_matches.getGpuMat().download(gpu_matches);
}
else
{
gpu_matches = _gpu_matches.getMat();
}
float _distance = *distance_ptr;
if (gpu_matches.empty())
{
matches.clear();
return;
}
DMatch m(queryIdx, _trainIdx, _imgIdx, _distance);
CV_Assert( (gpu_matches.type() == CV_32SC1) && (gpu_matches.rows == 2 || gpu_matches.rows == 3) );
matches.push_back(m);
}
}
const int nQuery = gpu_matches.cols;
void cv::cuda::BFMatcher_CUDA::match(const GpuMat& query, std::vector<DMatch>& matches, const std::vector<GpuMat>& masks)
{
GpuMat trainCollection;
GpuMat maskCollection;
matches.clear();
matches.reserve(nQuery);
makeGpuCollection(trainCollection, maskCollection, masks);
const int* trainIdxPtr = NULL;
const int* imgIdxPtr = NULL;
const float* distancePtr = NULL;
GpuMat trainIdx, imgIdx, distance;
if (gpu_matches.rows == 2)
{
trainIdxPtr = gpu_matches.ptr<int>(0);
distancePtr = gpu_matches.ptr<float>(1);
}
else
{
trainIdxPtr = gpu_matches.ptr<int>(0);
imgIdxPtr = gpu_matches.ptr<int>(1);
distancePtr = gpu_matches.ptr<float>(2);
}
matchCollection(query, trainCollection, trainIdx, imgIdx, distance, maskCollection);
matchDownload(trainIdx, imgIdx, distance, matches);
}
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
{
const int trainIdx = trainIdxPtr[queryIdx];
if (trainIdx == -1)
continue;
////////////////////////////////////////////////////////////////////
// KnnMatch
const int imgIdx = imgIdxPtr ? imgIdxPtr[queryIdx] : 0;
const float distance = distancePtr[queryIdx];
void cv::cuda::BFMatcher_CUDA::knnMatchSingle(const GpuMat& query, const GpuMat& train,
GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k,
const GpuMat& mask, Stream& stream)
{
if (query.empty() || train.empty())
return;
DMatch m(queryIdx, trainIdx, imgIdx, distance);
using namespace cv::cuda::device::bf_knnmatch;
matches.push_back(m);
}
}
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& train, int k, const PtrStepSzb& mask,
const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
cudaStream_t stream);
//
// knn match
//
static const caller_t callersL1[] =
void BFMatcher_Impl::knnMatch(InputArray _queryDescriptors, InputArray _trainDescriptors,
std::vector<std::vector<DMatch> >& matches,
int k,
InputArray _mask,
bool compactResult)
{
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
matchL1_gpu<int>, matchL1_gpu<float>
};
static const caller_t callersL2[] =
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
};
static const caller_t callersHamming[] =
GpuMat d_matches;
knnMatchAsync(_queryDescriptors, _trainDescriptors, d_matches, k, _mask);
knnMatchConvert(d_matches, matches, compactResult);
}
void BFMatcher_Impl::knnMatch(InputArray _queryDescriptors,
std::vector<std::vector<DMatch> >& matches,
int k,
const std::vector<GpuMat>& masks,
bool compactResult)
{
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
};
if (k == 2)
{
GpuMat d_matches;
knnMatchAsync(_queryDescriptors, d_matches, k, masks);
knnMatchConvert(d_matches, matches, compactResult);
}
else
{
const GpuMat query = _queryDescriptors.getGpuMat();
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(train.type() == query.type() && train.cols == query.cols);
CV_Assert(norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING);
if (query.empty() || trainDescCollection_.empty())
{
matches.clear();
return;
}
const caller_t* callers = norm == NORM_L1 ? callersL1 : norm == NORM_L2 ? callersL2 : callersHamming;
CV_Assert( query.channels() == 1 && query.depth() < CV_64F );
const int nQuery = query.rows;
const int nTrain = train.rows;
std::vector< std::vector<DMatch> > curMatches;
std::vector<DMatch> temp;
temp.reserve(2 * k);
if (k == 2)
{
ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);
}
else
{
ensureSizeIsEnough(nQuery, k, CV_32S, trainIdx);
ensureSizeIsEnough(nQuery, k, CV_32F, distance);
ensureSizeIsEnough(nQuery, nTrain, CV_32FC1, allDist);
}
matches.resize(query.rows);
for (size_t i = 0; i < matches.size(); ++i)
matches[i].reserve(k);
trainIdx.setTo(Scalar::all(-1), stream);
for (size_t imgIdx = 0; imgIdx < trainDescCollection_.size(); ++imgIdx)
{
knnMatch(query, trainDescCollection_[imgIdx], curMatches, k, masks.empty() ? GpuMat() : masks[imgIdx]);
caller_t func = callers[query.depth()];
CV_Assert(func != 0);
for (int queryIdx = 0; queryIdx < query.rows; ++queryIdx)
{
std::vector<DMatch>& localMatch = curMatches[queryIdx];
std::vector<DMatch>& globalMatch = matches[queryIdx];
func(query, train, k, mask, trainIdx, distance, allDist, StreamAccessor::getStream(stream));
}
for (size_t i = 0; i < localMatch.size(); ++i)
localMatch[i].imgIdx = imgIdx;
void cv::cuda::BFMatcher_CUDA::knnMatchDownload(const GpuMat& trainIdx, const GpuMat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult)
{
if (trainIdx.empty() || distance.empty())
return;
temp.clear();
std::merge(globalMatch.begin(), globalMatch.end(), localMatch.begin(), localMatch.end(), std::back_inserter(temp));
Mat trainIdxCPU(trainIdx);
Mat distanceCPU(distance);
globalMatch.clear();
const size_t count = std::min(static_cast<size_t>(k), temp.size());
std::copy(temp.begin(), temp.begin() + count, std::back_inserter(globalMatch));
}
}
knnMatchConvert(trainIdxCPU, distanceCPU, matches, compactResult);
}
if (compactResult)
{
std::vector< std::vector<DMatch> >::iterator new_end = std::remove_if(matches.begin(), matches.end(), std::mem_fun_ref(&std::vector<DMatch>::empty));
matches.erase(new_end, matches.end());
}
}
}
void cv::cuda::BFMatcher_CUDA::knnMatchConvert(const Mat& trainIdx, const Mat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult)
{
if (trainIdx.empty() || distance.empty())
return;
void BFMatcher_Impl::knnMatchAsync(InputArray _queryDescriptors, InputArray _trainDescriptors,
OutputArray _matches,
int k,
InputArray _mask,
Stream& stream)
{
using namespace cv::cuda::device::bf_knnmatch;
CV_Assert(trainIdx.type() == CV_32SC2 || trainIdx.type() == CV_32SC1);
CV_Assert(distance.type() == CV_32FC2 || distance.type() == CV_32FC1);
CV_Assert(distance.size() == trainIdx.size());
CV_Assert(trainIdx.isContinuous() && distance.isContinuous());
const GpuMat query = _queryDescriptors.getGpuMat();
const GpuMat train = _trainDescriptors.getGpuMat();
const GpuMat mask = _mask.getGpuMat();
if (query.empty() || train.empty())
{
_matches.release();
return;
}
const int nQuery = trainIdx.type() == CV_32SC2 ? trainIdx.cols : trainIdx.rows;
const int k = trainIdx.type() == CV_32SC2 ? 2 :trainIdx.cols;
CV_Assert( query.channels() == 1 && query.depth() < CV_64F );
CV_Assert( train.cols == query.cols && train.type() == query.type() );
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.rows == query.rows && mask.cols == train.rows) );
matches.clear();
matches.reserve(nQuery);
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& train, int k, const PtrStepSzb& mask,
const PtrStepSzb& trainIdx, const PtrStepSzb& distance, const PtrStepSzf& allDist,
cudaStream_t stream);
const int* trainIdx_ptr = trainIdx.ptr<int>();
const float* distance_ptr = distance.ptr<float>();
static const caller_t callersL1[] =
{
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
matchL1_gpu<int>, matchL1_gpu<float>
};
static const caller_t callersL2[] =
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
};
static const caller_t callersHamming[] =
{
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
};
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
{
matches.push_back(std::vector<DMatch>());
std::vector<DMatch>& curMatches = matches.back();
curMatches.reserve(k);
const caller_t* callers = norm_ == NORM_L1 ? callersL1 : norm_ == NORM_L2 ? callersL2 : callersHamming;
for (int i = 0; i < k; ++i, ++trainIdx_ptr, ++distance_ptr)
const caller_t func = callers[query.depth()];
if (func == 0)
{
int _trainIdx = *trainIdx_ptr;
CV_Error(Error::StsUnsupportedFormat, "unsupported combination of query.depth() and norm");
}
if (_trainIdx != -1)
{
float _distance = *distance_ptr;
const int nQuery = query.rows;
const int nTrain = train.rows;
DMatch m(queryIdx, _trainIdx, 0, _distance);
GpuMat trainIdx, distance, allDist;
if (k == 2)
{
_matches.create(2, nQuery, CV_32SC2);
GpuMat matches = _matches.getGpuMat();
curMatches.push_back(m);
}
trainIdx = GpuMat(1, nQuery, CV_32SC2, matches.ptr(0));
distance = GpuMat(1, nQuery, CV_32FC2, matches.ptr(1));
}
else
{
_matches.create(2 * nQuery, k, CV_32SC1);
GpuMat matches = _matches.getGpuMat();
if (compactResult && curMatches.empty())
matches.pop_back();
}
}
void cv::cuda::BFMatcher_CUDA::knnMatch(const GpuMat& query, const GpuMat& train,
std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask, bool compactResult)
{
GpuMat trainIdx, distance, allDist;
knnMatchSingle(query, train, trainIdx, distance, allDist, k, mask);
knnMatchDownload(trainIdx, distance, matches, compactResult);
}
trainIdx = GpuMat(nQuery, k, CV_32SC1, matches.ptr(0), matches.step);
distance = GpuMat(nQuery, k, CV_32FC1, matches.ptr(nQuery), matches.step);
void cv::cuda::BFMatcher_CUDA::knnMatch2Collection(const GpuMat& query, const GpuMat& trainCollection,
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
const GpuMat& maskCollection, Stream& stream)
{
if (query.empty() || trainCollection.empty())
return;
BufferPool pool(stream);
allDist = pool.getBuffer(nQuery, nTrain, CV_32FC1);
}
using namespace cv::cuda::device::bf_knnmatch;
trainIdx.setTo(Scalar::all(-1), stream);
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance,
cudaStream_t stream);
func(query, train, k, mask, trainIdx, distance, allDist, StreamAccessor::getStream(stream));
}
static const caller_t callersL1[] =
{
match2L1_gpu<unsigned char>, 0/*match2L1_gpu<signed char>*/,
match2L1_gpu<unsigned short>, match2L1_gpu<short>,
match2L1_gpu<int>, match2L1_gpu<float>
};
static const caller_t callersL2[] =
{
0/*match2L2_gpu<unsigned char>*/, 0/*match2L2_gpu<signed char>*/,
0/*match2L2_gpu<unsigned short>*/, 0/*match2L2_gpu<short>*/,
0/*match2L2_gpu<int>*/, match2L2_gpu<float>
};
static const caller_t callersHamming[] =
void BFMatcher_Impl::knnMatchAsync(InputArray _queryDescriptors,
OutputArray _matches,
int k,
const std::vector<GpuMat>& masks,
Stream& stream)
{
match2Hamming_gpu<unsigned char>, 0/*match2Hamming_gpu<signed char>*/,
match2Hamming_gpu<unsigned short>, 0/*match2Hamming_gpu<short>*/,
match2Hamming_gpu<int>, 0/*match2Hamming_gpu<float>*/
};
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING);
using namespace cv::cuda::device::bf_knnmatch;
const caller_t* callers = norm == NORM_L1 ? callersL1 : norm == NORM_L2 ? callersL2 : callersHamming;
if (k != 2)
{
CV_Error(Error::StsNotImplemented, "only k=2 mode is supported for now");
}
const int nQuery = query.rows;
const GpuMat query = _queryDescriptors.getGpuMat();
ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32SC2, imgIdx);
ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);
if (query.empty() || trainDescCollection_.empty())
{
_matches.release();
return;
}
trainIdx.setTo(Scalar::all(-1), stream);
CV_Assert( query.channels() == 1 && query.depth() < CV_64F );
caller_t func = callers[query.depth()];
CV_Assert(func != 0);
GpuMat trainCollection, maskCollection;
makeGpuCollection(trainDescCollection_, masks, trainCollection, maskCollection);
func(query, trainCollection, maskCollection, trainIdx, imgIdx, distance, StreamAccessor::getStream(stream));
}
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& trains, const PtrStepSz<PtrStepb>& masks,
const PtrStepSzb& trainIdx, const PtrStepSzb& imgIdx, const PtrStepSzb& distance,
cudaStream_t stream);
void cv::cuda::BFMatcher_CUDA::knnMatch2Download(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
return;
static const caller_t callersL1[] =
{
match2L1_gpu<unsigned char>, 0/*match2L1_gpu<signed char>*/,
match2L1_gpu<unsigned short>, match2L1_gpu<short>,
match2L1_gpu<int>, match2L1_gpu<float>
};
static const caller_t callersL2[] =
{
0/*match2L2_gpu<unsigned char>*/, 0/*match2L2_gpu<signed char>*/,
0/*match2L2_gpu<unsigned short>*/, 0/*match2L2_gpu<short>*/,
0/*match2L2_gpu<int>*/, match2L2_gpu<float>
};
static const caller_t callersHamming[] =
{
match2Hamming_gpu<unsigned char>, 0/*match2Hamming_gpu<signed char>*/,
match2Hamming_gpu<unsigned short>, 0/*match2Hamming_gpu<short>*/,
match2Hamming_gpu<int>, 0/*match2Hamming_gpu<float>*/
};
Mat trainIdxCPU(trainIdx);
Mat imgIdxCPU(imgIdx);
Mat distanceCPU(distance);
const caller_t* callers = norm_ == NORM_L1 ? callersL1 : norm_ == NORM_L2 ? callersL2 : callersHamming;
knnMatch2Convert(trainIdxCPU, imgIdxCPU, distanceCPU, matches, compactResult);
}
const caller_t func = callers[query.depth()];
if (func == 0)
{
CV_Error(Error::StsUnsupportedFormat, "unsupported combination of query.depth() and norm");
}
void cv::cuda::BFMatcher_CUDA::knnMatch2Convert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
return;
const int nQuery = query.rows;
CV_Assert(trainIdx.type() == CV_32SC2);
CV_Assert(imgIdx.type() == CV_32SC2 && imgIdx.cols == trainIdx.cols);
CV_Assert(distance.type() == CV_32FC2 && distance.cols == trainIdx.cols);
_matches.create(3, nQuery, CV_32SC2);
GpuMat matches = _matches.getGpuMat();
const int nQuery = trainIdx.cols;
GpuMat trainIdx(1, nQuery, CV_32SC2, matches.ptr(0));
GpuMat imgIdx(1, nQuery, CV_32SC2, matches.ptr(1));
GpuMat distance(1, nQuery, CV_32FC2, matches.ptr(2));
matches.clear();
matches.reserve(nQuery);
trainIdx.setTo(Scalar::all(-1), stream);
const int* trainIdx_ptr = trainIdx.ptr<int>();
const int* imgIdx_ptr = imgIdx.ptr<int>();
const float* distance_ptr = distance.ptr<float>();
func(query, trainCollection, maskCollection, trainIdx, imgIdx, distance, StreamAccessor::getStream(stream));
}
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
void BFMatcher_Impl::knnMatchConvert(InputArray _gpu_matches,
std::vector< std::vector<DMatch> >& matches,
bool compactResult)
{
matches.push_back(std::vector<DMatch>());
std::vector<DMatch>& curMatches = matches.back();
curMatches.reserve(2);
for (int i = 0; i < 2; ++i, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
Mat gpu_matches;
if (_gpu_matches.kind() == _InputArray::CUDA_GPU_MAT)
{
int _trainIdx = *trainIdx_ptr;
if (_trainIdx != -1)
{
int _imgIdx = *imgIdx_ptr;
float _distance = *distance_ptr;
DMatch m(queryIdx, _trainIdx, _imgIdx, _distance);
curMatches.push_back(m);
}
_gpu_matches.getGpuMat().download(gpu_matches);
}
else
{
gpu_matches = _gpu_matches.getMat();
}
if (compactResult && curMatches.empty())
matches.pop_back();
}
}
if (gpu_matches.empty())
{
matches.clear();
return;
}
namespace
{
struct ImgIdxSetter
{
explicit inline ImgIdxSetter(int imgIdx_) : imgIdx(imgIdx_) {}
inline void operator()(DMatch& m) const {m.imgIdx = imgIdx;}
int imgIdx;
};
}
CV_Assert( ((gpu_matches.type() == CV_32SC2) && (gpu_matches.rows == 2 || gpu_matches.rows == 3)) ||
(gpu_matches.type() == CV_32SC1) );
void cv::cuda::BFMatcher_CUDA::knnMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, int k,
const std::vector<GpuMat>& masks, bool compactResult)
{
if (k == 2)
{
GpuMat trainCollection;
GpuMat maskCollection;
int nQuery = -1, k = -1;
makeGpuCollection(trainCollection, maskCollection, masks);
const int* trainIdxPtr = NULL;
const int* imgIdxPtr = NULL;
const float* distancePtr = NULL;
GpuMat trainIdx, imgIdx, distance;
if (gpu_matches.type() == CV_32SC2)
{
nQuery = gpu_matches.cols;
k = 2;
knnMatch2Collection(query, trainCollection, trainIdx, imgIdx, distance, maskCollection);
knnMatch2Download(trainIdx, imgIdx, distance, matches);
}
else
{
if (query.empty() || empty())
return;
if (gpu_matches.rows == 2)
{
trainIdxPtr = gpu_matches.ptr<int>(0);
distancePtr = gpu_matches.ptr<float>(1);
}
else
{
trainIdxPtr = gpu_matches.ptr<int>(0);
imgIdxPtr = gpu_matches.ptr<int>(1);
distancePtr = gpu_matches.ptr<float>(2);
}
}
else
{
nQuery = gpu_matches.rows / 2;
k = gpu_matches.cols;
std::vector< std::vector<DMatch> > curMatches;
std::vector<DMatch> temp;
temp.reserve(2 * k);
trainIdxPtr = gpu_matches.ptr<int>(0);
distancePtr = gpu_matches.ptr<float>(nQuery);
}
matches.resize(query.rows);
for_each(matches.begin(), matches.end(), bind2nd(mem_fun_ref(&std::vector<DMatch>::reserve), k));
matches.clear();
matches.reserve(nQuery);
for (size_t imgIdx = 0, size = trainDescCollection.size(); imgIdx < size; ++imgIdx)
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
{
knnMatch(query, trainDescCollection[imgIdx], curMatches, k, masks.empty() ? GpuMat() : masks[imgIdx]);
matches.push_back(std::vector<DMatch>());
std::vector<DMatch>& curMatches = matches.back();
curMatches.reserve(k);
for (int queryIdx = 0; queryIdx < query.rows; ++queryIdx)
for (int i = 0; i < k; ++i)
{
std::vector<DMatch>& localMatch = curMatches[queryIdx];
std::vector<DMatch>& globalMatch = matches[queryIdx];
const int trainIdx = *trainIdxPtr;
if (trainIdx == -1)
continue;
for_each(localMatch.begin(), localMatch.end(), ImgIdxSetter(static_cast<int>(imgIdx)));
const int imgIdx = imgIdxPtr ? *imgIdxPtr : 0;
const float distance = *distancePtr;
temp.clear();
merge(globalMatch.begin(), globalMatch.end(), localMatch.begin(), localMatch.end(), back_inserter(temp));
DMatch m(queryIdx, trainIdx, imgIdx, distance);
curMatches.push_back(m);
globalMatch.clear();
const size_t count = std::min((size_t)k, temp.size());
copy(temp.begin(), temp.begin() + count, back_inserter(globalMatch));
++trainIdxPtr;
++distancePtr;
if (imgIdxPtr)
++imgIdxPtr;
}
}
if (compactResult)
{
std::vector< std::vector<DMatch> >::iterator new_end = remove_if(matches.begin(), matches.end(), mem_fun_ref(&std::vector<DMatch>::empty));
matches.erase(new_end, matches.end());
if (compactResult && curMatches.empty())
{
matches.pop_back();
}
}
}
}
////////////////////////////////////////////////////////////////////
// RadiusMatch
void cv::cuda::BFMatcher_CUDA::radiusMatchSingle(const GpuMat& query, const GpuMat& train,
GpuMat& trainIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
const GpuMat& mask, Stream& stream)
{
if (query.empty() || train.empty())
return;
using namespace cv::cuda::device::bf_radius_match;
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& train, float maxDistance, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
cudaStream_t stream);
//
// radius match
//
static const caller_t callersL1[] =
void BFMatcher_Impl::radiusMatch(InputArray _queryDescriptors, InputArray _trainDescriptors,
std::vector<std::vector<DMatch> >& matches,
float maxDistance,
InputArray _mask,
bool compactResult)
{
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
matchL1_gpu<int>, matchL1_gpu<float>
};
static const caller_t callersL2[] =
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
};
static const caller_t callersHamming[] =
{
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
};
GpuMat d_matches;
radiusMatchAsync(_queryDescriptors, _trainDescriptors, d_matches, maxDistance, _mask);
radiusMatchConvert(d_matches, matches, compactResult);
}
const int nQuery = query.rows;
const int nTrain = train.rows;
void BFMatcher_Impl::radiusMatch(InputArray _queryDescriptors,
std::vector<std::vector<DMatch> >& matches,
float maxDistance,
const std::vector<GpuMat>& masks,
bool compactResult)
{
GpuMat d_matches;
radiusMatchAsync(_queryDescriptors, d_matches, maxDistance, masks);
radiusMatchConvert(d_matches, matches, compactResult);
}
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(train.type() == query.type() && train.cols == query.cols);
CV_Assert(trainIdx.empty() || (trainIdx.rows == nQuery && trainIdx.size() == distance.size()));
CV_Assert(norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING);
void BFMatcher_Impl::radiusMatchAsync(InputArray _queryDescriptors, InputArray _trainDescriptors,
OutputArray _matches,
float maxDistance,
InputArray _mask,
Stream& stream)
{
using namespace cv::cuda::device::bf_radius_match;
const caller_t* callers = norm == NORM_L1 ? callersL1 : norm == NORM_L2 ? callersL2 : callersHamming;
const GpuMat query = _queryDescriptors.getGpuMat();
const GpuMat train = _trainDescriptors.getGpuMat();
const GpuMat mask = _mask.getGpuMat();
ensureSizeIsEnough(1, nQuery, CV_32SC1, nMatches);
if (trainIdx.empty())
{
ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32SC1, trainIdx);
ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32FC1, distance);
}
if (query.empty() || train.empty())
{
_matches.release();
return;
}
nMatches.setTo(Scalar::all(0), stream);
CV_Assert( query.channels() == 1 && query.depth() < CV_64F );
CV_Assert( train.cols == query.cols && train.type() == query.type() );
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.rows == query.rows && mask.cols == train.rows) );
caller_t func = callers[query.depth()];
CV_Assert(func != 0);
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb& train, float maxDistance, const PtrStepSzb& mask,
const PtrStepSzi& trainIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
cudaStream_t stream);
func(query, train, maxDistance, mask, trainIdx, distance, nMatches, StreamAccessor::getStream(stream));
}
static const caller_t callersL1[] =
{
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
matchL1_gpu<int>, matchL1_gpu<float>
};
static const caller_t callersL2[] =
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
};
static const caller_t callersHamming[] =
{
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
};
void cv::cuda::BFMatcher_CUDA::radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, const GpuMat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult)
{
if (trainIdx.empty() || distance.empty() || nMatches.empty())
return;
const caller_t* callers = norm_ == NORM_L1 ? callersL1 : norm_ == NORM_L2 ? callersL2 : callersHamming;
Mat trainIdxCPU(trainIdx);
Mat distanceCPU(distance);
Mat nMatchesCPU(nMatches);
const caller_t func = callers[query.depth()];
if (func == 0)
{
CV_Error(Error::StsUnsupportedFormat, "unsupported combination of query.depth() and norm");
}
radiusMatchConvert(trainIdxCPU, distanceCPU, nMatchesCPU, matches, compactResult);
}
const int nQuery = query.rows;
const int nTrain = train.rows;
void cv::cuda::BFMatcher_CUDA::radiusMatchConvert(const Mat& trainIdx, const Mat& distance, const Mat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult)
{
if (trainIdx.empty() || distance.empty() || nMatches.empty())
return;
const int cols = std::max((nTrain / 100), nQuery);
CV_Assert(trainIdx.type() == CV_32SC1);
CV_Assert(distance.type() == CV_32FC1 && distance.size() == trainIdx.size());
CV_Assert(nMatches.type() == CV_32SC1 && nMatches.cols == trainIdx.rows);
_matches.create(2 * nQuery + 1, cols, CV_32SC1);
GpuMat matches = _matches.getGpuMat();
const int nQuery = trainIdx.rows;
GpuMat trainIdx(nQuery, cols, CV_32SC1, matches.ptr(0), matches.step);
GpuMat distance(nQuery, cols, CV_32FC1, matches.ptr(nQuery), matches.step);
GpuMat nMatches(1, nQuery, CV_32SC1, matches.ptr(2 * nQuery));
matches.clear();
matches.reserve(nQuery);
nMatches.setTo(Scalar::all(0), stream);
const int* nMatches_ptr = nMatches.ptr<int>();
func(query, train, maxDistance, mask, trainIdx, distance, nMatches, StreamAccessor::getStream(stream));
}
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
void BFMatcher_Impl::radiusMatchAsync(InputArray _queryDescriptors,
OutputArray _matches,
float maxDistance,
const std::vector<GpuMat>& masks,
Stream& stream)
{
const int* trainIdx_ptr = trainIdx.ptr<int>(queryIdx);
const float* distance_ptr = distance.ptr<float>(queryIdx);
using namespace cv::cuda::device::bf_radius_match;
const int nMatched = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
const GpuMat query = _queryDescriptors.getGpuMat();
if (nMatched == 0)
if (query.empty() || trainDescCollection_.empty())
{
if (!compactResult)
matches.push_back(std::vector<DMatch>());
continue;
_matches.release();
return;
}
matches.push_back(std::vector<DMatch>(nMatched));
std::vector<DMatch>& curMatches = matches.back();
for (int i = 0; i < nMatched; ++i, ++trainIdx_ptr, ++distance_ptr)
{
int _trainIdx = *trainIdx_ptr;
CV_Assert( query.channels() == 1 && query.depth() < CV_64F );
float _distance = *distance_ptr;
GpuMat trainCollection, maskCollection;
makeGpuCollection(trainDescCollection_, masks, trainCollection, maskCollection);
DMatch m(queryIdx, _trainIdx, 0, _distance);
curMatches[i] = m;
}
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
cudaStream_t stream);
sort(curMatches.begin(), curMatches.end());
}
}
void cv::cuda::BFMatcher_CUDA::radiusMatch(const GpuMat& query, const GpuMat& train,
std::vector< std::vector<DMatch> >& matches, float maxDistance, const GpuMat& mask, bool compactResult)
{
GpuMat trainIdx, distance, nMatches;
radiusMatchSingle(query, train, trainIdx, distance, nMatches, maxDistance, mask);
radiusMatchDownload(trainIdx, distance, nMatches, matches, compactResult);
}
static const caller_t callersL1[] =
{
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
matchL1_gpu<int>, matchL1_gpu<float>
};
static const caller_t callersL2[] =
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
};
static const caller_t callersHamming[] =
{
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
};
void cv::cuda::BFMatcher_CUDA::radiusMatchCollection(const GpuMat& query, GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, GpuMat& nMatches,
float maxDistance, const std::vector<GpuMat>& masks, Stream& stream)
{
if (query.empty() || empty())
return;
const caller_t* callers = norm_ == NORM_L1 ? callersL1 : norm_ == NORM_L2 ? callersL2 : callersHamming;
using namespace cv::cuda::device::bf_radius_match;
const caller_t func = callers[query.depth()];
if (func == 0)
{
CV_Error(Error::StsUnsupportedFormat, "unsupported combination of query.depth() and norm");
}
typedef void (*caller_t)(const PtrStepSzb& query, const PtrStepSzb* trains, int n, float maxDistance, const PtrStepSzb* masks,
const PtrStepSzi& trainIdx, const PtrStepSzi& imgIdx, const PtrStepSzf& distance, const PtrStepSz<unsigned int>& nMatches,
cudaStream_t stream);
const int nQuery = query.rows;
static const caller_t callersL1[] =
{
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
matchL1_gpu<int>, matchL1_gpu<float>
};
static const caller_t callersL2[] =
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
};
static const caller_t callersHamming[] =
{
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
};
_matches.create(3 * nQuery + 1, nQuery, CV_32FC1);
GpuMat matches = _matches.getGpuMat();
const int nQuery = query.rows;
GpuMat trainIdx(nQuery, nQuery, CV_32SC1, matches.ptr(0), matches.step);
GpuMat imgIdx(nQuery, nQuery, CV_32SC1, matches.ptr(nQuery), matches.step);
GpuMat distance(nQuery, nQuery, CV_32FC1, matches.ptr(2 * nQuery), matches.step);
GpuMat nMatches(1, nQuery, CV_32SC1, matches.ptr(3 * nQuery));
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(trainIdx.empty() || (trainIdx.rows == nQuery && trainIdx.size() == distance.size() && trainIdx.size() == imgIdx.size()));
CV_Assert(norm == NORM_L1 || norm == NORM_L2 || norm == NORM_HAMMING);
nMatches.setTo(Scalar::all(0), stream);
const caller_t* callers = norm == NORM_L1 ? callersL1 : norm == NORM_L2 ? callersL2 : callersHamming;
std::vector<PtrStepSzb> trains_(trainDescCollection_.begin(), trainDescCollection_.end());
std::vector<PtrStepSzb> masks_(masks.begin(), masks.end());
ensureSizeIsEnough(1, nQuery, CV_32SC1, nMatches);
if (trainIdx.empty())
{
ensureSizeIsEnough(nQuery, std::max((nQuery / 100), 10), CV_32SC1, trainIdx);
ensureSizeIsEnough(nQuery, std::max((nQuery / 100), 10), CV_32SC1, imgIdx);
ensureSizeIsEnough(nQuery, std::max((nQuery / 100), 10), CV_32FC1, distance);
func(query, &trains_[0], static_cast<int>(trains_.size()), maxDistance, masks_.size() == 0 ? 0 : &masks_[0],
trainIdx, imgIdx, distance, nMatches, StreamAccessor::getStream(stream));
}
nMatches.setTo(Scalar::all(0), stream);
caller_t func = callers[query.depth()];
CV_Assert(func != 0);
std::vector<PtrStepSzb> trains_(trainDescCollection.begin(), trainDescCollection.end());
std::vector<PtrStepSzb> masks_(masks.begin(), masks.end());
void BFMatcher_Impl::radiusMatchConvert(InputArray _gpu_matches,
std::vector< std::vector<DMatch> >& matches,
bool compactResult)
{
Mat gpu_matches;
if (_gpu_matches.kind() == _InputArray::CUDA_GPU_MAT)
{
_gpu_matches.getGpuMat().download(gpu_matches);
}
else
{
gpu_matches = _gpu_matches.getMat();
}
func(query, &trains_[0], static_cast<int>(trains_.size()), maxDistance, masks_.size() == 0 ? 0 : &masks_[0],
trainIdx, imgIdx, distance, nMatches, StreamAccessor::getStream(stream));
}
if (gpu_matches.empty())
{
matches.clear();
return;
}
void cv::cuda::BFMatcher_CUDA::radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, const GpuMat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty() || nMatches.empty())
return;
CV_Assert( gpu_matches.type() == CV_32SC1 || gpu_matches.type() == CV_32FC1 );
Mat trainIdxCPU(trainIdx);
Mat imgIdxCPU(imgIdx);
Mat distanceCPU(distance);
Mat nMatchesCPU(nMatches);
int nQuery = -1;
radiusMatchConvert(trainIdxCPU, imgIdxCPU, distanceCPU, nMatchesCPU, matches, compactResult);
}
const int* trainIdxPtr = NULL;
const int* imgIdxPtr = NULL;
const float* distancePtr = NULL;
const int* nMatchesPtr = NULL;
void cv::cuda::BFMatcher_CUDA::radiusMatchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, const Mat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty() || nMatches.empty())
return;
if (gpu_matches.type() == CV_32SC1)
{
nQuery = (gpu_matches.rows - 1) / 2;
CV_Assert(trainIdx.type() == CV_32SC1);
CV_Assert(imgIdx.type() == CV_32SC1 && imgIdx.size() == trainIdx.size());
CV_Assert(distance.type() == CV_32FC1 && distance.size() == trainIdx.size());
CV_Assert(nMatches.type() == CV_32SC1 && nMatches.cols == trainIdx.rows);
trainIdxPtr = gpu_matches.ptr<int>(0);
distancePtr = gpu_matches.ptr<float>(nQuery);
nMatchesPtr = gpu_matches.ptr<int>(2 * nQuery);
}
else
{
nQuery = (gpu_matches.rows - 1) / 3;
const int nQuery = trainIdx.rows;
trainIdxPtr = gpu_matches.ptr<int>(0);
imgIdxPtr = gpu_matches.ptr<int>(nQuery);
distancePtr = gpu_matches.ptr<float>(2 * nQuery);
nMatchesPtr = gpu_matches.ptr<int>(3 * nQuery);
}
matches.clear();
matches.reserve(nQuery);
matches.clear();
matches.reserve(nQuery);
const int* nMatches_ptr = nMatches.ptr<int>();
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
{
const int nMatched = std::min(nMatchesPtr[queryIdx], gpu_matches.cols);
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
{
const int* trainIdx_ptr = trainIdx.ptr<int>(queryIdx);
const int* imgIdx_ptr = imgIdx.ptr<int>(queryIdx);
const float* distance_ptr = distance.ptr<float>(queryIdx);
if (nMatched == 0)
{
if (!compactResult)
{
matches.push_back(std::vector<DMatch>());
}
}
else
{
matches.push_back(std::vector<DMatch>(nMatched));
std::vector<DMatch>& curMatches = matches.back();
const int nMatched = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
for (int i = 0; i < nMatched; ++i)
{
const int trainIdx = trainIdxPtr[i];
if (nMatched == 0)
{
if (!compactResult)
matches.push_back(std::vector<DMatch>());
continue;
}
const int imgIdx = imgIdxPtr ? imgIdxPtr[i] : 0;
const float distance = distancePtr[i];
matches.push_back(std::vector<DMatch>());
std::vector<DMatch>& curMatches = matches.back();
curMatches.reserve(nMatched);
DMatch m(queryIdx, trainIdx, imgIdx, distance);
for (int i = 0; i < nMatched; ++i, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
{
int _trainIdx = *trainIdx_ptr;
int _imgIdx = *imgIdx_ptr;
float _distance = *distance_ptr;
curMatches[i] = m;
}
DMatch m(queryIdx, _trainIdx, _imgIdx, _distance);
std::sort(curMatches.begin(), curMatches.end());
}
curMatches.push_back(m);
trainIdxPtr += gpu_matches.cols;
distancePtr += gpu_matches.cols;
if (imgIdxPtr)
imgIdxPtr += gpu_matches.cols;
}
sort(curMatches.begin(), curMatches.end());
}
}
void cv::cuda::BFMatcher_CUDA::radiusMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches,
float maxDistance, const std::vector<GpuMat>& masks, bool compactResult)
Ptr<cv::cuda::DescriptorMatcher> cv::cuda::DescriptorMatcher::createBFMatcher(int norm)
{
GpuMat trainIdx, imgIdx, distance, nMatches;
radiusMatchCollection(query, trainIdx, imgIdx, distance, nMatches, maxDistance, masks);
radiusMatchDownload(trainIdx, imgIdx, distance, nMatches, matches, compactResult);
return makePtr<BFMatcher_Impl>(norm);
}
#endif /* !defined (HAVE_CUDA) */
modules/cudafeatures2d/test/test_features2d.cpp
View file @ 8a178da1
......@@ -285,7 +285,8 @@ PARAM_TEST_CASE(BruteForceMatcher, cv::cuda::DeviceInfo, NormCode, DescriptorSiz
CUDA_TEST_P(BruteForceMatcher, Match_Single)
{
cv::cuda::BFMatcher_CUDA matcher(normCode);
cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
cv::cuda::GpuMat mask;
if (useMask)
......@@ -295,7 +296,7 @@ CUDA_TEST_P(BruteForceMatcher, Match_Single)
}
std::vector<cv::DMatch> matches;
matcher.match(loadMat(query), loadMat(train), matches, mask);
matcher->match(loadMat(query), loadMat(train), matches, mask);
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
......@@ -312,13 +313,14 @@ CUDA_TEST_P(BruteForceMatcher, Match_Single)
CUDA_TEST_P(BruteForceMatcher, Match_Collection)
{
cv::cuda::BFMatcher_CUDA matcher(normCode);
cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
cv::cuda::GpuMat d_train(train);
// make add() twice to test such case
matcher.add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
matcher.add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
// prepare masks (make first nearest match illegal)
std::vector<cv::cuda::GpuMat> masks(2);
......@@ -331,9 +333,9 @@ CUDA_TEST_P(BruteForceMatcher, Match_Collection)
std::vector<cv::DMatch> matches;
if (useMask)
matcher.match(cv::cuda::GpuMat(query), matches, masks);
matcher->match(cv::cuda::GpuMat(query), matches, masks);
else
matcher.match(cv::cuda::GpuMat(query), matches);
matcher->match(cv::cuda::GpuMat(query), matches);
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
......@@ -366,7 +368,8 @@ CUDA_TEST_P(BruteForceMatcher, Match_Collection)
CUDA_TEST_P(BruteForceMatcher, KnnMatch_2_Single)
{
cv::cuda::BFMatcher_CUDA matcher(normCode);
cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
const int knn = 2;
......@@ -378,7 +381,7 @@ CUDA_TEST_P(BruteForceMatcher, KnnMatch_2_Single)
}
std::vector< std::vector<cv::DMatch> > matches;
matcher.knnMatch(loadMat(query), loadMat(train), matches, knn, mask);
matcher->knnMatch(loadMat(query), loadMat(train), matches, knn, mask);
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
......@@ -405,7 +408,8 @@ CUDA_TEST_P(BruteForceMatcher, KnnMatch_2_Single)
CUDA_TEST_P(BruteForceMatcher, KnnMatch_3_Single)
{
cv::cuda::BFMatcher_CUDA matcher(normCode);
cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
const int knn = 3;
......@@ -417,7 +421,7 @@ CUDA_TEST_P(BruteForceMatcher, KnnMatch_3_Single)
}
std::vector< std::vector<cv::DMatch> > matches;
matcher.knnMatch(loadMat(query), loadMat(train), matches, knn, mask);
matcher->knnMatch(loadMat(query), loadMat(train), matches, knn, mask);
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
......@@ -444,15 +448,16 @@ CUDA_TEST_P(BruteForceMatcher, KnnMatch_3_Single)
CUDA_TEST_P(BruteForceMatcher, KnnMatch_2_Collection)
{
cv::cuda::BFMatcher_CUDA matcher(normCode);
cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
const int knn = 2;
cv::cuda::GpuMat d_train(train);
// make add() twice to test such case
matcher.add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
matcher.add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
// prepare masks (make first nearest match illegal)
std::vector<cv::cuda::GpuMat> masks(2);
......@@ -466,9 +471,9 @@ CUDA_TEST_P(BruteForceMatcher, KnnMatch_2_Collection)
std::vector< std::vector<cv::DMatch> > matches;
if (useMask)
matcher.knnMatch(cv::cuda::GpuMat(query), matches, knn, masks);
matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn, masks);
else
matcher.knnMatch(cv::cuda::GpuMat(query), matches, knn);
matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn);
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
......@@ -506,15 +511,16 @@ CUDA_TEST_P(BruteForceMatcher, KnnMatch_2_Collection)
CUDA_TEST_P(BruteForceMatcher, KnnMatch_3_Collection)
{
cv::cuda::BFMatcher_CUDA matcher(normCode);
cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
const int knn = 3;
cv::cuda::GpuMat d_train(train);
// make add() twice to test such case
matcher.add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
matcher.add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
// prepare masks (make first nearest match illegal)
std::vector<cv::cuda::GpuMat> masks(2);
......@@ -528,9 +534,9 @@ CUDA_TEST_P(BruteForceMatcher, KnnMatch_3_Collection)
std::vector< std::vector<cv::DMatch> > matches;
if (useMask)
matcher.knnMatch(cv::cuda::GpuMat(query), matches, knn, masks);
matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn, masks);
else
matcher.knnMatch(cv::cuda::GpuMat(query), matches, knn);
matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn);
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
......@@ -568,7 +574,8 @@ CUDA_TEST_P(BruteForceMatcher, KnnMatch_3_Collection)
CUDA_TEST_P(BruteForceMatcher, RadiusMatch_Single)
{
cv::cuda::BFMatcher_CUDA matcher(normCode);
cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
const float radius = 1.f / countFactor;
......@@ -577,7 +584,7 @@ CUDA_TEST_P(BruteForceMatcher, RadiusMatch_Single)
try
{
std::vector< std::vector<cv::DMatch> > matches;
matcher.radiusMatch(loadMat(query), loadMat(train), matches, radius);
matcher->radiusMatch(loadMat(query), loadMat(train), matches, radius);
}
catch (const cv::Exception& e)
{
......@@ -594,7 +601,7 @@ CUDA_TEST_P(BruteForceMatcher, RadiusMatch_Single)
}
std::vector< std::vector<cv::DMatch> > matches;
matcher.radiusMatch(loadMat(query), loadMat(train), matches, radius, mask);
matcher->radiusMatch(loadMat(query), loadMat(train), matches, radius, mask);
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
......@@ -617,7 +624,8 @@ CUDA_TEST_P(BruteForceMatcher, RadiusMatch_Single)
CUDA_TEST_P(BruteForceMatcher, RadiusMatch_Collection)
{
cv::cuda::BFMatcher_CUDA matcher(normCode);
cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
const int n = 3;
const float radius = 1.f / countFactor * n;
......@@ -625,8 +633,8 @@ CUDA_TEST_P(BruteForceMatcher, RadiusMatch_Collection)
cv::cuda::GpuMat d_train(train);
// make add() twice to test such case
matcher.add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
matcher.add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
// prepare masks (make first nearest match illegal)
std::vector<cv::cuda::GpuMat> masks(2);
......@@ -642,7 +650,7 @@ CUDA_TEST_P(BruteForceMatcher, RadiusMatch_Collection)
try
{
std::vector< std::vector<cv::DMatch> > matches;
matcher.radiusMatch(cv::cuda::GpuMat(query), matches, radius, masks);
matcher->radiusMatch(cv::cuda::GpuMat(query), matches, radius, masks);
}
catch (const cv::Exception& e)
{
......@@ -654,9 +662,9 @@ CUDA_TEST_P(BruteForceMatcher, RadiusMatch_Collection)
std::vector< std::vector<cv::DMatch> > matches;
if (useMask)
matcher.radiusMatch(cv::cuda::GpuMat(query), matches, radius, masks);
matcher->radiusMatch(cv::cuda::GpuMat(query), matches, radius, masks);
else
matcher.radiusMatch(cv::cuda::GpuMat(query), matches, radius);
matcher->radiusMatch(cv::cuda::GpuMat(query), matches, radius);
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
......
modules/stitching/src/matchers.cpp
View file @ 8a178da1
......@@ -154,7 +154,7 @@ void CpuMatcher::match(const ImageFeatures &features1, const ImageFeatures &feat
matches_info.matches.clear();
Ptr<DescriptorMatcher> matcher;
Ptr<cv::DescriptorMatcher> matcher;
#if 0 // TODO check this
if (ocl::useOpenCL())
{
......@@ -220,13 +220,13 @@ void GpuMatcher::match(const ImageFeatures &features1, const ImageFeatures &feat
descriptors1_.upload(features1.descriptors);
descriptors2_.upload(features2.descriptors);
BFMatcher_CUDA matcher(NORM_L2);
Ptr<cuda::DescriptorMatcher> matcher = cuda::DescriptorMatcher::createBFMatcher(NORM_L2);
MatchesSet matches;
// Find 1->2 matches
pair_matches.clear();
matcher.knnMatchSingle(descriptors1_, descriptors2_, train_idx_, distance_, all_dist_, 2);
matcher.knnMatchDownload(train_idx_, distance_, pair_matches);
matcher->knnMatch(descriptors1_, descriptors2_, pair_matches, 2);
for (size_t i = 0; i < pair_matches.size(); ++i)
{
if (pair_matches[i].size() < 2)
......@@ -242,8 +242,7 @@ void GpuMatcher::match(const ImageFeatures &features1, const ImageFeatures &feat
// Find 2->1 matches
pair_matches.clear();
matcher.knnMatchSingle(descriptors2_, descriptors1_, train_idx_, distance_, all_dist_, 2);
matcher.knnMatchDownload(train_idx_, distance_, pair_matches);
matcher->knnMatch(descriptors2_, descriptors1_, pair_matches, 2);
for (size_t i = 0; i < pair_matches.size(); ++i)
{
if (pair_matches[i].size() < 2)
......
samples/gpu/performance/tests.cpp
View file @ 8a178da1
......@@ -379,14 +379,14 @@ TEST(BruteForceMatcher)
// Init CUDA matcher
cuda::BFMatcher_CUDA d_matcher(NORM_L2);
Ptr<cuda::DescriptorMatcher> d_matcher = cuda::DescriptorMatcher::createBFMatcher(NORM_L2);
cuda::GpuMat d_query(query);
cuda::GpuMat d_train(train);
// Output
vector< vector<DMatch> > matches(2);
cuda::GpuMat d_trainIdx, d_distance, d_allDist, d_nMatches;
cuda::GpuMat d_matches;
SUBTEST << "match";
......@@ -396,10 +396,10 @@ TEST(BruteForceMatcher)
matcher.match(query, train, matches[0]);
CPU_OFF;
d_matcher.matchSingle(d_query, d_train, d_trainIdx, d_distance);
d_matcher->matchAsync(d_query, d_train, d_matches);
CUDA_ON;
d_matcher.matchSingle(d_query, d_train, d_trainIdx, d_distance);
d_matcher->matchAsync(d_query, d_train, d_matches);
CUDA_OFF;
SUBTEST << "knnMatch";
......@@ -410,10 +410,10 @@ TEST(BruteForceMatcher)
matcher.knnMatch(query, train, matches, 2);
CPU_OFF;
d_matcher.knnMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_allDist, 2);
d_matcher->knnMatchAsync(d_query, d_train, d_matches, 2);
CUDA_ON;
d_matcher.knnMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_allDist, 2);
d_matcher->knnMatchAsync(d_query, d_train, d_matches, 2);
CUDA_OFF;
SUBTEST << "radiusMatch";
......@@ -426,12 +426,10 @@ TEST(BruteForceMatcher)
matcher.radiusMatch(query, train, matches, max_distance);
CPU_OFF;
d_trainIdx.release();
d_matcher.radiusMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_nMatches, max_distance);
d_matcher->radiusMatchAsync(d_query, d_train, d_matches, max_distance);
CUDA_ON;
d_matcher.radiusMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_nMatches, max_distance);
d_matcher->radiusMatchAsync(d_query, d_train, d_matches, max_distance);
CUDA_OFF;
}
......
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