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#ifndef __OPENCV_CUDA_HPP__
#define __OPENCV_CUDA_HPP__
#ifndef __cplusplus
# error cuda.hpp header must be compiled as C++
#endif
#include "opencv2/core/cuda.hpp"
namespace cv { namespace cuda {
//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
struct CV_EXPORTS HOGConfidence
{
double scale;
std::vector<Point> locations;
std::vector<double> confidences;
std::vector<double> part_scores[4];
};
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 std::vector<float>& detector);
static std::vector<float> getDefaultPeopleDetector();
static std::vector<float> getPeopleDetector48x96();
static std::vector<float> getPeopleDetector64x128();
void detect(const GpuMat& img, std::vector<Point>& found_locations,
double hit_threshold=0, Size win_stride=Size(),
Size padding=Size());
void detectMultiScale(const GpuMat& img, std::vector<Rect>& found_locations,
double hit_threshold=0, Size win_stride=Size(),
Size padding=Size(), double scale0=1.05,
int group_threshold=2);
void computeConfidence(const GpuMat& img, std::vector<Point>& hits, double hit_threshold,
Size win_stride, Size padding, std::vector<Point>& locations, std::vector<double>& confidences);
void computeConfidenceMultiScale(const GpuMat& img, std::vector<Rect>& found_locations,
double hit_threshold, Size win_stride, Size padding,
std::vector<HOGConfidence> &conf_out, int group_threshold);
void getDescriptors(const GpuMat& img, Size win_stride,
GpuMat& 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 GpuMat& img);
void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& 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;
GpuMat detector;
// Results of the last classification step
GpuMat labels, labels_buf;
Mat labels_host;
// Results of the last histogram evaluation step
GpuMat block_hists, block_hists_buf;
// Gradients conputation results
GpuMat grad, qangle, grad_buf, qangle_buf;
// returns subbuffer with required size, reallocates buffer if nessesary.
static GpuMat getBuffer(const Size& sz, int type, GpuMat& buf);
static GpuMat getBuffer(int rows, int cols, int type, GpuMat& buf);
std::vector<GpuMat> image_scales;
};
//////////////////////////// CascadeClassifier ////////////////////////////
// The cascade classifier class for object detection: supports old haar and new lbp xlm formats and nvbin for haar cascades olny.
class CV_EXPORTS CascadeClassifier_CUDA
{
public:
CascadeClassifier_CUDA();
CascadeClassifier_CUDA(const String& filename);
~CascadeClassifier_CUDA();
bool empty() const;
bool load(const String& filename);
void release();
/* returns number of detected objects */
int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor = 1.2, int minNeighbors = 4, Size minSize = Size());
int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4);
bool findLargestObject;
bool visualizeInPlace;
Size getClassifierSize() const;
private:
struct CascadeClassifierImpl;
CascadeClassifierImpl* impl;
struct HaarCascade;
struct LbpCascade;
friend class CascadeClassifier_CUDA_LBP;
};
//////////////////////////// Labeling ////////////////////////////
//!performs labeling via graph cuts of a 2D regular 4-connected graph.
CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels,
GpuMat& buf, Stream& stream = Stream::Null());
//!performs labeling via graph cuts of a 2D regular 8-connected graph.
CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& topLeft, GpuMat& topRight,
GpuMat& bottom, GpuMat& bottomLeft, GpuMat& bottomRight,
GpuMat& labels,
GpuMat& buf, Stream& stream = Stream::Null());
//! compute mask for Generalized Flood fill componetns labeling.
CV_EXPORTS void connectivityMask(const GpuMat& image, GpuMat& mask, const cv::Scalar& lo, const cv::Scalar& hi, Stream& stream = Stream::Null());
//! performs connected componnents labeling.
CV_EXPORTS void labelComponents(const GpuMat& mask, GpuMat& components, int flags = 0, Stream& stream = Stream::Null());
//////////////////////////// Calib3d ////////////////////////////
CV_EXPORTS void transformPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
GpuMat& dst, Stream& stream = Stream::Null());
CV_EXPORTS void projectPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
const Mat& camera_mat, const Mat& dist_coef, GpuMat& dst,
Stream& stream = Stream::Null());
CV_EXPORTS void solvePnPRansac(const Mat& object, const Mat& image, const Mat& camera_mat,
const Mat& dist_coef, Mat& rvec, Mat& tvec, bool use_extrinsic_guess=false,
int num_iters=100, float max_dist=8.0, int min_inlier_count=100,
std::vector<int>* inliers=NULL);
//////////////////////////// VStab ////////////////////////////
//! removes points (CV_32FC2, single row matrix) with zero mask value
CV_EXPORTS void compactPoints(GpuMat &points0, GpuMat &points1, const GpuMat &mask);
CV_EXPORTS void calcWobbleSuppressionMaps(
int left, int idx, int right, Size size, const Mat &ml, const Mat &mr,
GpuMat &mapx, GpuMat &mapy);
}} // namespace cv { namespace cuda {
#endif /* __OPENCV_CUDA_HPP__ */