gpucalib3d module for camera calibration and stereo correspondence
This commit is contained in:
Vladislav Vinogradov
@@ -55,6 +55,7 @@
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#include "opencv2/gpuimgproc.hpp"
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#include "opencv2/gpufeatures2d.hpp"
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#include "opencv2/gpuvideo.hpp"
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#include "opencv2/gpucalib3d.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/objdetect.hpp"
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@@ -68,18 +69,6 @@ namespace cv { namespace gpu {
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///////////////////////////// Calibration 3D //////////////////////////////////
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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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//////////////////////////////// Image Labeling ////////////////////////////////
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@@ -90,190 +79,17 @@ CV_EXPORTS void solvePnPRansac(const Mat& object, const Mat& image, const Mat& c
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//////////////////////////////// StereoBM_GPU ////////////////////////////////
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class CV_EXPORTS StereoBM_GPU
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{
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public:
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enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 };
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enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };
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//! the default constructor
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StereoBM_GPU();
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//! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8.
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StereoBM_GPU(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ);
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//! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
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//! Output disparity has CV_8U type.
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void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());
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//! Some heuristics that tries to estmate
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// if current GPU will be faster than CPU in this algorithm.
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// It queries current active device.
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static bool checkIfGpuCallReasonable();
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int preset;
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int ndisp;
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int winSize;
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// If avergeTexThreshold == 0 => post procesing is disabled
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// If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image
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// SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold
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// i.e. input left image is low textured.
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float avergeTexThreshold;
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private:
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GpuMat minSSD, leBuf, riBuf;
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};
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////////////////////////// StereoBeliefPropagation ///////////////////////////
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// "Efficient Belief Propagation for Early Vision"
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// P.Felzenszwalb
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class CV_EXPORTS StereoBeliefPropagation
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{
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public:
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enum { DEFAULT_NDISP = 64 };
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enum { DEFAULT_ITERS = 5 };
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enum { DEFAULT_LEVELS = 5 };
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static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels);
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//! the default constructor
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explicit StereoBeliefPropagation(int ndisp = DEFAULT_NDISP,
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int iters = DEFAULT_ITERS,
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int levels = DEFAULT_LEVELS,
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int msg_type = CV_32F);
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//! the full constructor taking the number of disparities, number of BP iterations on each level,
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//! number of levels, truncation of data cost, data weight,
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//! truncation of discontinuity cost and discontinuity single jump
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//! DataTerm = data_weight * min(fabs(I2-I1), max_data_term)
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//! DiscTerm = min(disc_single_jump * fabs(f1-f2), max_disc_term)
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//! please see paper for more details
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StereoBeliefPropagation(int ndisp, int iters, int levels,
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float max_data_term, float data_weight,
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float max_disc_term, float disc_single_jump,
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int msg_type = CV_32F);
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//! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair,
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//! if disparity is empty output type will be CV_16S else output type will be disparity.type().
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void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());
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//! version for user specified data term
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void operator()(const GpuMat& data, GpuMat& disparity, Stream& stream = Stream::Null());
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int ndisp;
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int iters;
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int levels;
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float max_data_term;
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float data_weight;
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float max_disc_term;
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float disc_single_jump;
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int msg_type;
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private:
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GpuMat u, d, l, r, u2, d2, l2, r2;
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std::vector<GpuMat> datas;
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GpuMat out;
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};
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/////////////////////////// StereoConstantSpaceBP ///////////////////////////
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// "A Constant-Space Belief Propagation Algorithm for Stereo Matching"
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// Qingxiong Yang, Liang Wang, Narendra Ahuja
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// http://vision.ai.uiuc.edu/~qyang6/
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class CV_EXPORTS StereoConstantSpaceBP
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{
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public:
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enum { DEFAULT_NDISP = 128 };
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enum { DEFAULT_ITERS = 8 };
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enum { DEFAULT_LEVELS = 4 };
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enum { DEFAULT_NR_PLANE = 4 };
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static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels, int& nr_plane);
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//! the default constructor
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explicit StereoConstantSpaceBP(int ndisp = DEFAULT_NDISP,
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int iters = DEFAULT_ITERS,
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int levels = DEFAULT_LEVELS,
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int nr_plane = DEFAULT_NR_PLANE,
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int msg_type = CV_32F);
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//! the full constructor taking the number of disparities, number of BP iterations on each level,
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//! number of levels, number of active disparity on the first level, truncation of data cost, data weight,
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//! truncation of discontinuity cost, discontinuity single jump and minimum disparity threshold
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StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane,
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float max_data_term, float data_weight, float max_disc_term, float disc_single_jump,
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int min_disp_th = 0,
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int msg_type = CV_32F);
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//! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair,
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//! if disparity is empty output type will be CV_16S else output type will be disparity.type().
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void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());
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int ndisp;
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int iters;
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int levels;
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int nr_plane;
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float max_data_term;
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float data_weight;
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float max_disc_term;
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float disc_single_jump;
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int min_disp_th;
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int msg_type;
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bool use_local_init_data_cost;
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private:
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GpuMat messages_buffers;
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GpuMat temp;
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GpuMat out;
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};
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/////////////////////////// DisparityBilateralFilter ///////////////////////////
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// Disparity map refinement using joint bilateral filtering given a single color image.
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// Qingxiong Yang, Liang Wang, Narendra Ahuja
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// http://vision.ai.uiuc.edu/~qyang6/
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class CV_EXPORTS DisparityBilateralFilter
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{
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public:
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enum { DEFAULT_NDISP = 64 };
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enum { DEFAULT_RADIUS = 3 };
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enum { DEFAULT_ITERS = 1 };
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//! the default constructor
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explicit DisparityBilateralFilter(int ndisp = DEFAULT_NDISP, int radius = DEFAULT_RADIUS, int iters = DEFAULT_ITERS);
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//! the full constructor taking the number of disparities, filter radius,
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//! number of iterations, truncation of data continuity, truncation of disparity continuity
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//! and filter range sigma
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DisparityBilateralFilter(int ndisp, int radius, int iters, float edge_threshold, float max_disc_threshold, float sigma_range);
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//! the disparity map refinement operator. Refine disparity map using joint bilateral filtering given a single color image.
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//! disparity must have CV_8U or CV_16S type, image must have CV_8UC1 or CV_8UC3 type.
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void operator()(const GpuMat& disparity, const GpuMat& image, GpuMat& dst, Stream& stream = Stream::Null());
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private:
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int ndisp;
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int radius;
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int iters;
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float edge_threshold;
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float max_disc_threshold;
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float sigma_range;
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GpuMat table_color;
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GpuMat table_space;
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};
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//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
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