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update some of the functions in ocl module to the latest version

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yao
2012-09-03 17:03:37 +08:00
parent 3fb3851c7a
commit 0fdb55a54d
24 changed files with 2931 additions and 1728 deletions
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353
modules/ocl/include/opencv2/ocl/ocl.hpp
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@@ -877,32 +877,32 @@ 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() {}
explicit CannyBuf(const Size& image_size, int apperture_size = 3) {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;
oclMat counter;
Ptr<FilterEngine_GPU> filterDX, filterDY;
///////////////////////////////////////////// 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() {}
explicit CannyBuf(const Size& image_size, int apperture_size = 3) {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;
};
#ifdef HAVE_CLAMDFFT
@@ -935,154 +935,161 @@ namespace cv
const oclMat& src3, double beta, oclMat& dst, int flags = 0);
#endif
//////////////// 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 };
//////////////// 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;
};
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:
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;
std::vector<oclMat> image_scales;
};
//! Speeded up robust features, port from GPU module.
////////////////////////////////// SURF //////////////////////////////////////////
class CV_EXPORTS SURF_OCL
{
public:
enum KeypointLayout
{
X_ROW = 0,
Y_ROW,
LAPLACIAN_ROW,
OCTAVE_ROW,
SIZE_ROW,
ANGLE_ROW,
HESSIAN_ROW,
ROWS_COUNT
};
//! the default constructor
SURF_OCL();
//! the full constructor taking all the necessary parameters
explicit SURF_OCL(double _hessianThreshold, int _nOctaves=4,
int _nOctaveLayers=2, bool _extended=false, float _keypointsRatio=0.01f, bool _upright = false);
//! returns the descriptor size in float's (64 or 128)
int descriptorSize() const;
//! upload host keypoints to device memory
void uploadKeypoints(const vector<cv::KeyPoint>& keypoints, oclMat& keypointsocl);
//! download keypoints from device to host memory
void downloadKeypoints(const oclMat& keypointsocl, vector<KeyPoint>& keypoints);
//! download descriptors from device to host memory
void downloadDescriptors(const oclMat& descriptorsocl, vector<float>& descriptors);
//! finds the keypoints using fast hessian detector used in SURF
//! supports CV_8UC1 images
//! keypoints will have nFeature cols and 6 rows
//! keypoints.ptr<float>(X_ROW)[i] will contain x coordinate of i'th feature
//! keypoints.ptr<float>(Y_ROW)[i] will contain y coordinate of i'th feature
//! keypoints.ptr<float>(LAPLACIAN_ROW)[i] will contain laplacian sign of i'th feature
//! keypoints.ptr<float>(OCTAVE_ROW)[i] will contain octave of i'th feature
//! keypoints.ptr<float>(SIZE_ROW)[i] will contain size of i'th feature
//! keypoints.ptr<float>(ANGLE_ROW)[i] will contain orientation of i'th feature
//! keypoints.ptr<float>(HESSIAN_ROW)[i] will contain response of i'th feature
void operator()(const oclMat& img, const oclMat& mask, oclMat& keypoints);
//! finds the keypoints and computes their descriptors.
//! Optionally it can compute descriptors for the user-provided keypoints and recompute keypoints direction
void operator()(const oclMat& img, const oclMat& mask, oclMat& keypoints, oclMat& descriptors,
bool useProvidedKeypoints = false);
void operator()(const oclMat& img, const oclMat& mask, std::vector<KeyPoint>& keypoints);
void operator()(const oclMat& img, const oclMat& mask, std::vector<KeyPoint>& keypoints, oclMat& descriptors,
bool useProvidedKeypoints = false);
void operator()(const oclMat& img, const oclMat& mask, std::vector<KeyPoint>& keypoints, std::vector<float>& descriptors,
bool useProvidedKeypoints = false);
void releaseMemory();
// SURF parameters
float hessianThreshold;
int nOctaves;
int nOctaveLayers;
bool extended;
bool upright;
//! max keypoints = min(keypointsRatio * img.size().area(), 65535)
float keypointsRatio;
oclMat sum, mask1, maskSum, intBuffer;
oclMat det, trace;
oclMat maxPosBuffer;
};
//! Speeded up robust features, port from GPU module.
////////////////////////////////// SURF //////////////////////////////////////////
class CV_EXPORTS SURF_OCL
{
public:
enum KeypointLayout
{
X_ROW = 0,
Y_ROW,
LAPLACIAN_ROW,
OCTAVE_ROW,
SIZE_ROW,
ANGLE_ROW,
HESSIAN_ROW,
ROWS_COUNT
};
//! the default constructor
SURF_OCL();
//! the full constructor taking all the necessary parameters
explicit SURF_OCL(double _hessianThreshold, int _nOctaves=4,
int _nOctaveLayers=2, bool _extended=false, float _keypointsRatio=0.01f, bool _upright = false);
//! returns the descriptor size in float's (64 or 128)
int descriptorSize() const;
//! upload host keypoints to device memory
void uploadKeypoints(const vector<cv::KeyPoint>& keypoints, oclMat& keypointsocl);
//! download keypoints from device to host memory
void downloadKeypoints(const oclMat& keypointsocl, vector<KeyPoint>& keypoints);
//! download descriptors from device to host memory
void downloadDescriptors(const oclMat& descriptorsocl, vector<float>& descriptors);
//! finds the keypoints using fast hessian detector used in SURF
//! supports CV_8UC1 images
//! keypoints will have nFeature cols and 6 rows
//! keypoints.ptr<float>(X_ROW)[i] will contain x coordinate of i'th feature
//! keypoints.ptr<float>(Y_ROW)[i] will contain y coordinate of i'th feature
//! keypoints.ptr<float>(LAPLACIAN_ROW)[i] will contain laplacian sign of i'th feature
//! keypoints.ptr<float>(OCTAVE_ROW)[i] will contain octave of i'th feature
//! keypoints.ptr<float>(SIZE_ROW)[i] will contain size of i'th feature
//! keypoints.ptr<float>(ANGLE_ROW)[i] will contain orientation of i'th feature
//! keypoints.ptr<float>(HESSIAN_ROW)[i] will contain response of i'th feature
void operator()(const oclMat& img, const oclMat& mask, oclMat& keypoints);
//! finds the keypoints and computes their descriptors.
//! Optionally it can compute descriptors for the user-provided keypoints and recompute keypoints direction
void operator()(const oclMat& img, const oclMat& mask, oclMat& keypoints, oclMat& descriptors,
bool useProvidedKeypoints = false);
void operator()(const oclMat& img, const oclMat& mask, std::vector<KeyPoint>& keypoints);
void operator()(const oclMat& img, const oclMat& mask, std::vector<KeyPoint>& keypoints, oclMat& descriptors,
bool useProvidedKeypoints = false);
void operator()(const oclMat& img, const oclMat& mask, std::vector<KeyPoint>& keypoints, std::vector<float>& descriptors,
bool useProvidedKeypoints = false);
void releaseMemory();
// SURF parameters
float hessianThreshold;
int nOctaves;
int nOctaveLayers;
bool extended;
bool upright;
//! max keypoints = min(keypointsRatio * img.size().area(), 65535)
float keypointsRatio;
oclMat sum, mask1, maskSum, intBuffer;
oclMat det, trace;
oclMat maxPosBuffer;
};
}
}
#include "opencv2/ocl/matrix_operations.hpp"