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refactor CUDA BFMatcher algorithm:

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use new abstract interface and hidden implementation
This commit is contained in:
Vladislav Vinogradov 2015-01-13 17:57:30 +03:00
parent 764d55b81d
commit 8a178da1a4
6 changed files with 1269 additions and 1053 deletions
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411
modules/cudafeatures2d/include/opencv2/cudafeatures2d.hpp
View File

@ -63,170 +63,315 @@ namespace cv { namespace cuda {
//! @addtogroup cudafeatures2d
//! @{
/** @brief Brute-force descriptor matcher.
//
// DescriptorMatcher
//
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.
/** @brief Abstract base class for matching keypoint descriptors.
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.
@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);
//
// Factories
//
//! Add descriptors to train descriptor collection
void add(const std::vector<GpuMat>& descCollection);
/** @brief Brute-force descriptor matcher.
//! Get train descriptors collection
const std::vector<GpuMat>& getTrainDescriptors() const;
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.
//! Clear train descriptors collection
void clear();
@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);
//! Return true if there are not train descriptors in collection
bool empty() const;
//
// Utility
//
//! Return true if the matcher supports mask in match methods
bool isMaskSupported() const;
/** @brief Returns true if the descriptor matcher supports masking permissible matches.
*/
virtual bool isMaskSupported() const = 0;
//! 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());
//
// Descriptor collection
//
//! 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);
/** @brief Adds descriptors to train a descriptor collection.
//! Find one best match for each query descriptor
void match(const GpuMat& query, const GpuMat& train, std::vector<DMatch>& matches, const GpuMat& mask = GpuMat());
If the collection is not empty, the new descriptors are added to existing train descriptors.
//! 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>());
@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;
//! 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());
/** @brief Returns a constant link to the train descriptor collection.
*/
virtual const std::vector<GpuMat>& getTrainDescriptors() const = 0;
//! 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);
/** @brief Clears the train descriptor collection.
*/
virtual void clear() = 0;
//! 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>());
/** @brief Returns true if there are no train descriptors in the collection.
*/
virtual bool empty() const = 0;
//! 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());
/** @brief Trains a descriptor matcher.
//! 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);
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;
//! 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);
//
// 1 to 1 match
//
//! 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());
/** @brief Finds the best match for each descriptor from a query set (blocking version).
//! 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);
@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.
//! 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);
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;
//! 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());
/** @overload
*/
virtual void match(InputArray queryDescriptors,
std::vector<DMatch>& matches,
const std::vector<GpuMat>& masks = std::vector<GpuMat>()) = 0;
//! 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);
/** @brief Finds the best match for each descriptor from a query set (asynchronous version).
//! 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);
@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.
//! 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());
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;
//! 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);
/** @overload
*/
virtual void matchAsync(InputArray queryDescriptors,
OutputArray matches,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
Stream& stream = Stream::Null()) = 0;
//! 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);
/** @brief Converts matches array from internal representation to standard matches vector.
int norm;
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).
private:
std::vector<GpuMat> trainDescCollection;
@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;
};
//

24
modules/cudafeatures2d/perf/perf_features2d.cpp
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@ -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);

1790
modules/cudafeatures2d/src/brute_force_matcher.cpp
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File diff suppressed because it is too large Load Diff

68
modules/cudafeatures2d/test/test_features2d.cpp
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@ -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());

11
modules/stitching/src/matchers.cpp
View File

@ -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)

18
samples/gpu/performance/tests.cpp
View File

@ -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;
}