generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Clean up spaces in ocl.hpp

Browse Source
This commit is contained in:
peng xiao 2013-05-15 10:43:47 +08:00
parent 87fcd2342f
commit 3f93c3cc4e
1 changed files with 0 additions and 372 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

372
modules/ocl/include/opencv2/ocl/ocl.hpp
View File

@ -866,7 +866,6 @@ namespace cv
std::vector<oclMat> image_sqsums;
};
//! computes the proximity map for the raster template and the image where the template is searched for
// Supports TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED for type 8UC1 and 8UC4
// Supports TM_SQDIFF, TM_CCORR for type 32FC1 and 32FC4
@ -877,71 +876,36 @@ namespace cv
// Supports TM_SQDIFF, TM_CCORR for type 32FC1 and 32FC4
CV_EXPORTS void matchTemplate(const oclMat &image, const oclMat &templ, oclMat &result, int method, MatchTemplateBuf &buf);
///////////////////////////////////////////// Canny /////////////////////////////////////////////
struct CV_EXPORTS CannyBuf;
//! compute edges of the input image using Canny operator
// Support CV_8UC1 only
CV_EXPORTS void Canny(const oclMat &image, oclMat &edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
CV_EXPORTS void Canny(const oclMat &image, CannyBuf &buf, oclMat &edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
CV_EXPORTS void Canny(const oclMat &dx, const oclMat &dy, oclMat &edges, double low_thresh, double high_thresh, bool L2gradient = false);
CV_EXPORTS void Canny(const oclMat &dx, const oclMat &dy, CannyBuf &buf, oclMat &edges, double low_thresh, double high_thresh, bool L2gradient = false);
struct CV_EXPORTS CannyBuf
{
CannyBuf() : counter(NULL) {}
~CannyBuf()
{
release();
}
explicit CannyBuf(const Size &image_size, int apperture_size = 3) : counter(NULL)
{
create(image_size, apperture_size);
}
CannyBuf(const oclMat &dx_, const oclMat &dy_);
void create(const Size &image_size, int apperture_size = 3);
void release();
oclMat dx, dy;
oclMat dx_buf, dy_buf;
oclMat edgeBuf;
oclMat trackBuf1, trackBuf2;
void *counter;
Ptr<FilterEngine_GPU> filterDX, filterDY;
};
///////////////////////////////////////// clAmdFft related /////////////////////////////////////////
@ -966,159 +930,69 @@ namespace cv
const oclMat &src3, double beta, oclMat &dst, int flags = 0);
//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
struct CV_EXPORTS HOGDescriptor
{
enum { DEFAULT_WIN_SIGMA = -1 };
enum { DEFAULT_NLEVELS = 64 };
enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
HOGDescriptor(Size win_size = Size(64, 128), Size block_size = Size(16, 16),
Size block_stride = Size(8, 8), Size cell_size = Size(8, 8),
int nbins = 9, double win_sigma = DEFAULT_WIN_SIGMA,
double threshold_L2hys = 0.2, bool gamma_correction = true,
int nlevels = DEFAULT_NLEVELS);
size_t getDescriptorSize() const;
size_t getBlockHistogramSize() const;
void setSVMDetector(const vector<float> &detector);
static vector<float> getDefaultPeopleDetector();
static vector<float> getPeopleDetector48x96();
static vector<float> getPeopleDetector64x128();
void detect(const oclMat &img, vector<Point> &found_locations,
double hit_threshold = 0, Size win_stride = Size(),
Size padding = Size());
void detectMultiScale(const oclMat &img, vector<Rect> &found_locations,
double hit_threshold = 0, Size win_stride = Size(),
Size padding = Size(), double scale0 = 1.05,
int group_threshold = 2);
void getDescriptors(const oclMat &img, Size win_stride,
oclMat &descriptors,
int descr_format = DESCR_FORMAT_COL_BY_COL);
Size win_size;
Size block_size;
Size block_stride;
Size cell_size;
int nbins;
double win_sigma;
double threshold_L2hys;
bool gamma_correction;
int nlevels;
protected:
// initialize buffers; only need to do once in case of multiscale detection
void init_buffer(const oclMat &img, Size win_stride);
void computeBlockHistograms(const oclMat &img);
void computeGradient(const oclMat &img, oclMat &grad, oclMat &qangle);
double getWinSigma() const;
bool checkDetectorSize() const;
static int numPartsWithin(int size, int part_size, int stride);
static Size numPartsWithin(Size size, Size part_size, Size stride);
// Coefficients of the separating plane
float free_coef;
oclMat detector;
// Results of the last classification step
oclMat labels;
Mat labels_host;
// Results of the last histogram evaluation step
oclMat block_hists;
// Gradients conputation results
oclMat grad, qangle;
// scaled image
oclMat image_scale;
// effect size of input image (might be different from original size after scaling)
Size effect_size;
};
@ -1126,13 +1000,11 @@ namespace cv
/****************************************************************************************\
* Distance *
\****************************************************************************************/
template<typename T>
struct CV_EXPORTS Accumulator
{
typedef T Type;
};
template<> struct Accumulator<unsigned char>
{
typedef float Type;
@ -1206,469 +1078,225 @@ namespace cv
{
public:
enum DistType {L1Dist = 0, L2Dist, HammingDist};
explicit BruteForceMatcher_OCL_base(DistType distType = L2Dist);
// Add descriptors to train descriptor collection
void add(const std::vector<oclMat> &descCollection);
// Get train descriptors collection
const std::vector<oclMat> &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 oclMat &query, const oclMat &train,
oclMat &trainIdx, oclMat &distance,
const oclMat &mask = oclMat());
// Download trainIdx and distance and convert it to CPU vector with DMatch
static void matchDownload(const oclMat &trainIdx, const oclMat &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 oclMat &query, const oclMat &train, std::vector<DMatch> &matches, const oclMat &mask = oclMat());
// Make gpu collection of trains and masks in suitable format for matchCollection function
void makeGpuCollection(oclMat &trainCollection, oclMat &maskCollection, const std::vector<oclMat> &masks = std::vector<oclMat>());
// Find one best match from train collection for each query descriptor
void matchCollection(const oclMat &query, const oclMat &trainCollection,
oclMat &trainIdx, oclMat &imgIdx, oclMat &distance,
const oclMat &masks = oclMat());
// Download trainIdx, imgIdx and distance and convert it to vector with DMatch
static void matchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &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 oclMat &query, std::vector<DMatch> &matches, const std::vector<oclMat> &masks = std::vector<oclMat>());
// Find k best matches for each query descriptor (in increasing order of distances)
void knnMatchSingle(const oclMat &query, const oclMat &train,
oclMat &trainIdx, oclMat &distance, oclMat &allDist, int k,
const oclMat &mask = oclMat());
// 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 oclMat &trainIdx, const oclMat &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 oclMat &query, const oclMat &train,
std::vector< std::vector<DMatch> > &matches, int k, const oclMat &mask = oclMat(),
bool compactResult = false);
// Find k best matches from train collection for each query descriptor (in increasing order of distances)
void knnMatch2Collection(const oclMat &query, const oclMat &trainCollection,
oclMat &trainIdx, oclMat &imgIdx, oclMat &distance,
const oclMat &maskCollection = oclMat());
// 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 knnMatch2Download(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance,
std::vector< std::vector<DMatch> > &matches, bool compactResult = false);
// Convert trainIdx and distance to vector with DMatch
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 oclMat &query, std::vector< std::vector<DMatch> > &matches, int k,
const std::vector<oclMat> &masks = std::vector<oclMat>(), 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 oclMat &query, const oclMat &train,
oclMat &trainIdx, oclMat &distance, oclMat &nMatches, float maxDistance,
const oclMat &mask = oclMat());
// 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 oclMat &trainIdx, const oclMat &distance, const oclMat &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 oclMat &query, const oclMat &train,
std::vector< std::vector<DMatch> > &matches, float maxDistance,
const oclMat &mask = oclMat(), 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 oclMat &query, oclMat &trainIdx, oclMat &imgIdx, oclMat &distance, oclMat &nMatches, float maxDistance,
const std::vector<oclMat> &masks = std::vector<oclMat>());
// 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 oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance, const oclMat &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 oclMat &query, std::vector< std::vector<DMatch> > &matches, float maxDistance,
const std::vector<oclMat> &masks = std::vector<oclMat>(), bool compactResult = false);
DistType distType;
private:
std::vector<oclMat> trainDescCollection;
};
template <class Distance>
class CV_EXPORTS BruteForceMatcher_OCL;
template <typename T>
class CV_EXPORTS BruteForceMatcher_OCL< L1<T> > : public BruteForceMatcher_OCL_base
{
public:
explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L1Dist) {}
explicit BruteForceMatcher_OCL(L1<T> /*d*/) : BruteForceMatcher_OCL_base(L1Dist) {}
};
template <typename T>
class CV_EXPORTS BruteForceMatcher_OCL< L2<T> > : public BruteForceMatcher_OCL_base
{
public:
explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L2Dist) {}
explicit BruteForceMatcher_OCL(L2<T> /*d*/) : BruteForceMatcher_OCL_base(L2Dist) {}
};
template <> class CV_EXPORTS BruteForceMatcher_OCL< Hamming > : public BruteForceMatcher_OCL_base
{
public:
explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(HammingDist) {}
explicit BruteForceMatcher_OCL(Hamming /*d*/) : BruteForceMatcher_OCL_base(HammingDist) {}
};
/////////////////////////////// PyrLKOpticalFlow /////////////////////////////////////
class CV_EXPORTS PyrLKOpticalFlow
{
public:
PyrLKOpticalFlow()
{
winSize = Size(21, 21);
maxLevel = 3;
iters = 30;
derivLambda = 0.5;
useInitialFlow = false;
minEigThreshold = 1e-4f;
getMinEigenVals = false;
isDeviceArch11_ = false;
}
void sparse(const oclMat &prevImg, const oclMat &nextImg, const oclMat &prevPts, oclMat &nextPts,
oclMat &status, oclMat *err = 0);
void dense(const oclMat &prevImg, const oclMat &nextImg, oclMat &u, oclMat &v, oclMat *err = 0);
Size winSize;
int maxLevel;
int iters;
double derivLambda;
bool useInitialFlow;
float minEigThreshold;
bool getMinEigenVals;
void releaseMemory()
{
dx_calcBuf_.release();
dy_calcBuf_.release();
prevPyr_.clear();
nextPyr_.clear();
dx_buf_.release();
dy_buf_.release();
}
private:
void calcSharrDeriv(const oclMat &src, oclMat &dx, oclMat &dy);
void buildImagePyramid(const oclMat &img0, vector<oclMat> &pyr, bool withBorder);
oclMat dx_calcBuf_;
oclMat dy_calcBuf_;
vector<oclMat> prevPyr_;
vector<oclMat> nextPyr_;
oclMat dx_buf_;
oclMat dy_buf_;
oclMat uPyr_[2];
oclMat vPyr_[2];
bool isDeviceArch11_;
};
//////////////// build warping maps ////////////////////
//! builds plane warping maps