renmaed gpu module -> cuda
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Vladislav Vinogradov
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modules/cuda/include/opencv2/cuda.hpp
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modules/cuda/include/opencv2/cuda.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef __OPENCV_CUDA_HPP__
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#define __OPENCV_CUDA_HPP__
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#ifndef __cplusplus
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# error cuda.hpp header must be compiled as C++
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#endif
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#include "opencv2/core/cuda.hpp"
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namespace cv { namespace cuda {
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//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
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struct CV_EXPORTS HOGConfidence
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{
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double scale;
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std::vector<Point> locations;
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std::vector<double> confidences;
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std::vector<double> part_scores[4];
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};
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struct CV_EXPORTS HOGDescriptor
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{
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enum { DEFAULT_WIN_SIGMA = -1 };
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enum { DEFAULT_NLEVELS = 64 };
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enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
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HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16),
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Size block_stride=Size(8, 8), Size cell_size=Size(8, 8),
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int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA,
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double threshold_L2hys=0.2, bool gamma_correction=true,
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int nlevels=DEFAULT_NLEVELS);
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size_t getDescriptorSize() const;
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size_t getBlockHistogramSize() const;
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void setSVMDetector(const std::vector<float>& detector);
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static std::vector<float> getDefaultPeopleDetector();
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static std::vector<float> getPeopleDetector48x96();
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static std::vector<float> getPeopleDetector64x128();
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void detect(const GpuMat& img, std::vector<Point>& found_locations,
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double hit_threshold=0, Size win_stride=Size(),
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Size padding=Size());
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void detectMultiScale(const GpuMat& img, std::vector<Rect>& found_locations,
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double hit_threshold=0, Size win_stride=Size(),
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Size padding=Size(), double scale0=1.05,
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int group_threshold=2);
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void computeConfidence(const GpuMat& img, std::vector<Point>& hits, double hit_threshold,
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Size win_stride, Size padding, std::vector<Point>& locations, std::vector<double>& confidences);
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void computeConfidenceMultiScale(const GpuMat& img, std::vector<Rect>& found_locations,
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double hit_threshold, Size win_stride, Size padding,
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std::vector<HOGConfidence> &conf_out, int group_threshold);
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void getDescriptors(const GpuMat& img, Size win_stride,
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GpuMat& descriptors,
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int descr_format=DESCR_FORMAT_COL_BY_COL);
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Size win_size;
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Size block_size;
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Size block_stride;
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Size cell_size;
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int nbins;
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double win_sigma;
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double threshold_L2hys;
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bool gamma_correction;
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int nlevels;
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protected:
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void computeBlockHistograms(const GpuMat& img);
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void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle);
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double getWinSigma() const;
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bool checkDetectorSize() const;
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static int numPartsWithin(int size, int part_size, int stride);
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static Size numPartsWithin(Size size, Size part_size, Size stride);
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// Coefficients of the separating plane
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float free_coef;
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GpuMat detector;
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// Results of the last classification step
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GpuMat labels, labels_buf;
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Mat labels_host;
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// Results of the last histogram evaluation step
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GpuMat block_hists, block_hists_buf;
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// Gradients conputation results
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GpuMat grad, qangle, grad_buf, qangle_buf;
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// returns subbuffer with required size, reallocates buffer if nessesary.
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static GpuMat getBuffer(const Size& sz, int type, GpuMat& buf);
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static GpuMat getBuffer(int rows, int cols, int type, GpuMat& buf);
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std::vector<GpuMat> image_scales;
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};
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//////////////////////////// CascadeClassifier ////////////////////////////
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// The cascade classifier class for object detection: supports old haar and new lbp xlm formats and nvbin for haar cascades olny.
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class CV_EXPORTS CascadeClassifier_GPU
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{
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public:
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CascadeClassifier_GPU();
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CascadeClassifier_GPU(const String& filename);
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~CascadeClassifier_GPU();
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bool empty() const;
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bool load(const String& filename);
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void release();
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/* returns number of detected objects */
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int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor = 1.2, int minNeighbors = 4, Size minSize = Size());
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int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4);
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bool findLargestObject;
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bool visualizeInPlace;
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Size getClassifierSize() const;
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private:
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struct CascadeClassifierImpl;
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CascadeClassifierImpl* impl;
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struct HaarCascade;
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struct LbpCascade;
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friend class CascadeClassifier_GPU_LBP;
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};
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//////////////////////////// Labeling ////////////////////////////
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//!performs labeling via graph cuts of a 2D regular 4-connected graph.
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CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels,
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GpuMat& buf, Stream& stream = Stream::Null());
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//!performs labeling via graph cuts of a 2D regular 8-connected graph.
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CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& topLeft, GpuMat& topRight,
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GpuMat& bottom, GpuMat& bottomLeft, GpuMat& bottomRight,
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GpuMat& labels,
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GpuMat& buf, Stream& stream = Stream::Null());
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//! compute mask for Generalized Flood fill componetns labeling.
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CV_EXPORTS void connectivityMask(const GpuMat& image, GpuMat& mask, const cv::Scalar& lo, const cv::Scalar& hi, Stream& stream = Stream::Null());
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//! performs connected componnents labeling.
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CV_EXPORTS void labelComponents(const GpuMat& mask, GpuMat& components, int flags = 0, Stream& stream = Stream::Null());
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//////////////////////////// Calib3d ////////////////////////////
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CV_EXPORTS void transformPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
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GpuMat& dst, Stream& stream = Stream::Null());
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CV_EXPORTS void projectPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
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const Mat& camera_mat, const Mat& dist_coef, GpuMat& dst,
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Stream& stream = Stream::Null());
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CV_EXPORTS void solvePnPRansac(const Mat& object, const Mat& image, const Mat& camera_mat,
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const Mat& dist_coef, Mat& rvec, Mat& tvec, bool use_extrinsic_guess=false,
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int num_iters=100, float max_dist=8.0, int min_inlier_count=100,
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std::vector<int>* inliers=NULL);
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//////////////////////////// VStab ////////////////////////////
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//! removes points (CV_32FC2, single row matrix) with zero mask value
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CV_EXPORTS void compactPoints(GpuMat &points0, GpuMat &points1, const GpuMat &mask);
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CV_EXPORTS void calcWobbleSuppressionMaps(
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int left, int idx, int right, Size size, const Mat &ml, const Mat &mr,
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GpuMat &mapx, GpuMat &mapy);
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}} // namespace cv { namespace cuda {
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#endif /* __OPENCV_CUDA_HPP__ */
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