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提交 fc1fa285 编写于 作者: V Vladislav Vinogradov
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gpuvideo module for video processing

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modules/gpu/CMakeLists.txt
浏览文件 @ fc1fa285
......@@ -4,7 +4,9 @@ endif()
set(the_description "GPU-accelerated Computer Vision")
ocv_add_module(gpu opencv_imgproc opencv_calib3d opencv_objdetect opencv_video opencv_photo opencv_legacy opencv_gpuarithm opencv_gpufilters opencv_gpuimgproc opencv_gpufeatures2d OPTIONAL opencv_gpunvidia)
ocv_add_module(gpu opencv_imgproc opencv_calib3d opencv_objdetect opencv_video opencv_photo opencv_legacy
opencv_gpuarithm opencv_gpufilters opencv_gpuimgproc opencv_gpufeatures2d opencv_gpuvideo
OPTIONAL opencv_gpunvidia)
ocv_module_include_directories("${CMAKE_CURRENT_SOURCE_DIR}/src/cuda")
......
modules/gpu/doc/gpu.rst
浏览文件 @ fc1fa285
......@@ -10,4 +10,3 @@ gpu. GPU-accelerated Computer Vision
data_structures
object_detection
camera_calibration_and_3d_reconstruction
video
modules/gpu/include/opencv2/gpu.hpp
浏览文件 @ fc1fa285
......@@ -54,6 +54,7 @@
#include "opencv2/gpufilters.hpp"
#include "opencv2/gpuimgproc.hpp"
#include "opencv2/gpufeatures2d.hpp"
#include "opencv2/gpuvideo.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/objdetect.hpp"
......@@ -433,543 +434,23 @@ private:
////////////////////////////////// Optical Flow //////////////////////////////////////////
class CV_EXPORTS BroxOpticalFlow
{
public:
BroxOpticalFlow(float alpha_, float gamma_, float scale_factor_, int inner_iterations_, int outer_iterations_, int solver_iterations_) :
alpha(alpha_), gamma(gamma_), scale_factor(scale_factor_),
inner_iterations(inner_iterations_), outer_iterations(outer_iterations_), solver_iterations(solver_iterations_)
{
}
//! Compute optical flow
//! frame0 - source frame (supports only CV_32FC1 type)
//! frame1 - frame to track (with the same size and type as frame0)
//! u - flow horizontal component (along x axis)
//! v - flow vertical component (along y axis)
void operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& stream = Stream::Null());
//! flow smoothness
float alpha;
//! gradient constancy importance
float gamma;
//! pyramid scale factor
float scale_factor;
//! number of lagged non-linearity iterations (inner loop)
int inner_iterations;
//! number of warping iterations (number of pyramid levels)
int outer_iterations;
//! number of linear system solver iterations
int solver_iterations;
GpuMat buf;
};
class CV_EXPORTS PyrLKOpticalFlow
{
public:
PyrLKOpticalFlow();
void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
GpuMat& status, GpuMat* err = 0);
void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0);
void releaseMemory();
Size winSize;
int maxLevel;
int iters;
bool useInitialFlow;
private:
std::vector<GpuMat> prevPyr_;
std::vector<GpuMat> nextPyr_;
GpuMat buf_;
GpuMat uPyr_[2];
GpuMat vPyr_[2];
};
class CV_EXPORTS FarnebackOpticalFlow
{
public:
FarnebackOpticalFlow()
{
numLevels = 5;
pyrScale = 0.5;
fastPyramids = false;
winSize = 13;
numIters = 10;
polyN = 5;
polySigma = 1.1;
flags = 0;
}
int numLevels;
double pyrScale;
bool fastPyramids;
int winSize;
int numIters;
int polyN;
double polySigma;
int flags;
void operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null());
void releaseMemory()
{
frames_[0].release();
frames_[1].release();
pyrLevel_[0].release();
pyrLevel_[1].release();
M_.release();
bufM_.release();
R_[0].release();
R_[1].release();
blurredFrame_[0].release();
blurredFrame_[1].release();
pyramid0_.clear();
pyramid1_.clear();
}
private:
void prepareGaussian(
int n, double sigma, float *g, float *xg, float *xxg,
double &ig11, double &ig03, double &ig33, double &ig55);
void setPolynomialExpansionConsts(int n, double sigma);
void updateFlow_boxFilter(
const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat &flowy,
GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);
void updateFlow_gaussianBlur(
const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat& flowy,
GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);
GpuMat frames_[2];
GpuMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2];
std::vector<GpuMat> pyramid0_, pyramid1_;
};
// Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method
//
// see reference:
// [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
// [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
class CV_EXPORTS OpticalFlowDual_TVL1_GPU
{
public:
OpticalFlowDual_TVL1_GPU();
void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy);
void collectGarbage();
/**
* Time step of the numerical scheme.
*/
double tau;
/**
* Weight parameter for the data term, attachment parameter.
* This is the most relevant parameter, which determines the smoothness of the output.
* The smaller this parameter is, the smoother the solutions we obtain.
* It depends on the range of motions of the images, so its value should be adapted to each image sequence.
*/
double lambda;
/**
* Weight parameter for (u - v)^2, tightness parameter.
* It serves as a link between the attachment and the regularization terms.
* In theory, it should have a small value in order to maintain both parts in correspondence.
* The method is stable for a large range of values of this parameter.
*/
double theta;
/**
* Number of scales used to create the pyramid of images.
*/
int nscales;
/**
* Number of warpings per scale.
* Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale.
* This is a parameter that assures the stability of the method.
* It also affects the running time, so it is a compromise between speed and accuracy.
*/
int warps;
/**
* Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time.
* A small value will yield more accurate solutions at the expense of a slower convergence.
*/
double epsilon;
/**
* Stopping criterion iterations number used in the numerical scheme.
*/
int iterations;
double scaleStep;
bool useInitialFlow;
private:
void procOneScale(const GpuMat& I0, const GpuMat& I1, GpuMat& u1, GpuMat& u2);
std::vector<GpuMat> I0s;
std::vector<GpuMat> I1s;
std::vector<GpuMat> u1s;
std::vector<GpuMat> u2s;
GpuMat I1x_buf;
GpuMat I1y_buf;
GpuMat I1w_buf;
GpuMat I1wx_buf;
GpuMat I1wy_buf;
GpuMat grad_buf;
GpuMat rho_c_buf;
GpuMat p11_buf;
GpuMat p12_buf;
GpuMat p21_buf;
GpuMat p22_buf;
GpuMat diff_buf;
GpuMat norm_buf;
};
//! Calculates optical flow for 2 images using block matching algorithm */
CV_EXPORTS void calcOpticalFlowBM(const GpuMat& prev, const GpuMat& curr,
Size block_size, Size shift_size, Size max_range, bool use_previous,
GpuMat& velx, GpuMat& vely, GpuMat& buf,
Stream& stream = Stream::Null());
class CV_EXPORTS FastOpticalFlowBM
{
public:
void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy, int search_window = 21, int block_window = 7, Stream& s = Stream::Null());
private:
GpuMat buffer;
GpuMat extended_I0;
GpuMat extended_I1;
};
//! Interpolate frames (images) using provided optical flow (displacement field).
//! frame0 - frame 0 (32-bit floating point images, single channel)
//! frame1 - frame 1 (the same type and size)
//! fu - forward horizontal displacement
//! fv - forward vertical displacement
//! bu - backward horizontal displacement
//! bv - backward vertical displacement
//! pos - new frame position
//! newFrame - new frame
//! buf - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 GpuMat;
//! occlusion masks 0, occlusion masks 1,
//! interpolated forward flow 0, interpolated forward flow 1,
//! interpolated backward flow 0, interpolated backward flow 1
//!
CV_EXPORTS void interpolateFrames(const GpuMat& frame0, const GpuMat& frame1,
const GpuMat& fu, const GpuMat& fv,
const GpuMat& bu, const GpuMat& bv,
float pos, GpuMat& newFrame, GpuMat& buf,
Stream& stream = Stream::Null());
CV_EXPORTS void createOpticalFlowNeedleMap(const GpuMat& u, const GpuMat& v, GpuMat& vertex, GpuMat& colors);
//////////////////////// Background/foreground segmentation ////////////////////////
// Foreground Object Detection from Videos Containing Complex Background.
// Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian.
// ACM MM2003 9p
class CV_EXPORTS FGDStatModel
{
public:
struct CV_EXPORTS Params
{
int Lc; // Quantized levels per 'color' component. Power of two, typically 32, 64 or 128.
int N1c; // Number of color vectors used to model normal background color variation at a given pixel.
int N2c; // Number of color vectors retained at given pixel. Must be > N1c, typically ~ 5/3 of N1c.
// Used to allow the first N1c vectors to adapt over time to changing background.
int Lcc; // Quantized levels per 'color co-occurrence' component. Power of two, typically 16, 32 or 64.
int N1cc; // Number of color co-occurrence vectors used to model normal background color variation at a given pixel.
int N2cc; // Number of color co-occurrence vectors retained at given pixel. Must be > N1cc, typically ~ 5/3 of N1cc.
// Used to allow the first N1cc vectors to adapt over time to changing background.
bool is_obj_without_holes; // If TRUE we ignore holes within foreground blobs. Defaults to TRUE.
int perform_morphing; // Number of erode-dilate-erode foreground-blob cleanup iterations.
// These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1.
float alpha1; // How quickly we forget old background pixel values seen. Typically set to 0.1.
float alpha2; // "Controls speed of feature learning". Depends on T. Typical value circa 0.005.
float alpha3; // Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1.
float delta; // Affects color and color co-occurrence quantization, typically set to 2.
float T; // A percentage value which determines when new features can be recognized as new background. (Typically 0.9).
float minArea; // Discard foreground blobs whose bounding box is smaller than this threshold.
// default Params
Params();
};
// out_cn - channels count in output result (can be 3 or 4)
// 4-channels require more memory, but a bit faster
explicit FGDStatModel(int out_cn = 3);
explicit FGDStatModel(const cv::gpu::GpuMat& firstFrame, const Params& params = Params(), int out_cn = 3);
~FGDStatModel();
void create(const cv::gpu::GpuMat& firstFrame, const Params& params = Params());
void release();
int update(const cv::gpu::GpuMat& curFrame);
//8UC3 or 8UC4 reference background image
cv::gpu::GpuMat background;
//8UC1 foreground image
cv::gpu::GpuMat foreground;
std::vector< std::vector<cv::Point> > foreground_regions;
private:
FGDStatModel(const FGDStatModel&);
FGDStatModel& operator=(const FGDStatModel&);
class Impl;
std::auto_ptr<Impl> impl_;
};
/*!
Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm
The class implements the following algorithm:
"An improved adaptive background mixture model for real-time tracking with shadow detection"
P. KadewTraKuPong and R. Bowden,
Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
*/
class CV_EXPORTS MOG_GPU
{
public:
//! the default constructor
MOG_GPU(int nmixtures = -1);
//! re-initiaization method
void initialize(Size frameSize, int frameType);
//! the update operator
void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = 0.0f, Stream& stream = Stream::Null());
//! computes a background image which are the mean of all background gaussians
void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const;
//! releases all inner buffers
void release();
int history;
float varThreshold;
float backgroundRatio;
float noiseSigma;
private:
int nmixtures_;
Size frameSize_;
int frameType_;
int nframes_;
GpuMat weight_;
GpuMat sortKey_;
GpuMat mean_;
GpuMat var_;
};
/*!
The class implements the following algorithm:
"Improved adaptive Gausian mixture model for background subtraction"
Z.Zivkovic
International Conference Pattern Recognition, UK, August, 2004.
http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
*/
class CV_EXPORTS MOG2_GPU
{
public:
//! the default constructor
MOG2_GPU(int nmixtures = -1);
//! re-initiaization method
void initialize(Size frameSize, int frameType);
//! the update operator
void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());
//! computes a background image which are the mean of all background gaussians
void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const;
//! releases all inner buffers
void release();
// parameters
// you should call initialize after parameters changes
int history;
//! here it is the maximum allowed number of mixture components.
//! Actual number is determined dynamically per pixel
float varThreshold;
// threshold on the squared Mahalanobis distance to decide if it is well described
// by the background model or not. Related to Cthr from the paper.
// This does not influence the update of the background. A typical value could be 4 sigma
// and that is varThreshold=4*4=16; Corresponds to Tb in the paper.
/////////////////////////
// less important parameters - things you might change but be carefull
////////////////////////
float backgroundRatio;
// corresponds to fTB=1-cf from the paper
// TB - threshold when the component becomes significant enough to be included into
// the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
// For alpha=0.001 it means that the mode should exist for approximately 105 frames before
// it is considered foreground
// float noiseSigma;
float varThresholdGen;
//correspondts to Tg - threshold on the squared Mahalan. dist. to decide
//when a sample is close to the existing components. If it is not close
//to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
//Smaller Tg leads to more generated components and higher Tg might make
//lead to small number of components but they can grow too large
float fVarInit;
float fVarMin;
float fVarMax;
//initial variance for the newly generated components.
//It will will influence the speed of adaptation. A good guess should be made.
//A simple way is to estimate the typical standard deviation from the images.
//I used here 10 as a reasonable value
// min and max can be used to further control the variance
float fCT; //CT - complexity reduction prior
//this is related to the number of samples needed to accept that a component
//actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
//the standard Stauffer&Grimson algorithm (maybe not exact but very similar)
//shadow detection parameters
bool bShadowDetection; //default 1 - do shadow detection
unsigned char nShadowDetection; //do shadow detection - insert this value as the detection result - 127 default value
float fTau;
// Tau - shadow threshold. The shadow is detected if the pixel is darker
//version of the background. Tau is a threshold on how much darker the shadow can be.
//Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
//See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
private:
int nmixtures_;
Size frameSize_;
int frameType_;
int nframes_;
GpuMat weight_;
GpuMat variance_;
GpuMat mean_;
GpuMat bgmodelUsedModes_; //keep track of number of modes per pixel
};
/**
* Background Subtractor module. Takes a series of images and returns a sequence of mask (8UC1)
* images of the same size, where 255 indicates Foreground and 0 represents Background.
* This class implements an algorithm described in "Visual Tracking of Human Visitors under
* Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere,
* A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012.
*/
class CV_EXPORTS GMG_GPU
{
public:
GMG_GPU();
/**
* Validate parameters and set up data structures for appropriate frame size.
* @param frameSize Input frame size
* @param min Minimum value taken on by pixels in image sequence. Usually 0
* @param max Maximum value taken on by pixels in image sequence. e.g. 1.0 or 255
*/
void initialize(Size frameSize, float min = 0.0f, float max = 255.0f);
/**
* Performs single-frame background subtraction and builds up a statistical background image
* model.
* @param frame Input frame
* @param fgmask Output mask image representing foreground and background pixels
* @param stream Stream for the asynchronous version
*/
void operator ()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());
//! Releases all inner buffers
void release();
//! Total number of distinct colors to maintain in histogram.
int maxFeatures;
//! Set between 0.0 and 1.0, determines how quickly features are "forgotten" from histograms.
float learningRate;
//! Number of frames of video to use to initialize histograms.
int numInitializationFrames;
//! Number of discrete levels in each channel to be used in histograms.
int quantizationLevels;
//! Prior probability that any given pixel is a background pixel. A sensitivity parameter.
float backgroundPrior;
//! Value above which pixel is determined to be FG.
float decisionThreshold;
//! Smoothing radius, in pixels, for cleaning up FG image.
int smoothingRadius;
//! Perform background model update.
bool updateBackgroundModel;
private:
float maxVal_, minVal_;
Size frameSize_;
int frameNum_;
GpuMat nfeatures_;
GpuMat colors_;
GpuMat weights_;
Ptr<FilterEngine_GPU> boxFilter_;
GpuMat buf_;
};
//! removes points (CV_32FC2, single row matrix) with zero mask value
CV_EXPORTS void compactPoints(GpuMat &points0, GpuMat &points1, const GpuMat &mask);
......
modules/gpunvidia/include/opencv2/gpunvidia.hpp
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......@@ -46,7 +46,6 @@
#include "opencv2/gpunvidia/NCV.hpp"
#include "opencv2/gpunvidia/NPP_staging.hpp"
#include "opencv2/gpunvidia/NCVPyramid.hpp"
#include "opencv2/gpunvidia/NCVBroxOpticalFlow.hpp"
#include "opencv2/gpunvidia/NCVHaarObjectDetection.hpp"
#endif /* __OPENCV_GPUNVIDIA_HPP__ */
modules/gpuvideo/CMakeLists.txt 0 → 100644
浏览文件 @ fc1fa285
if(ANDROID OR IOS)
ocv_module_disable(gpuvideo)
endif()
set(the_description "GPU-accelerated Video Analysis")
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef -Wmissing-declarations)
ocv_define_module(gpuvideo opencv_video opencv_legacy opencv_gpufilters opencv_gpuimgproc OPTIONAL opencv_gpunvidia)
modules/gpuvideo/doc/gpuvideo.rst 0 → 100644
浏览文件 @ fc1fa285
***********************************
gpu. GPU-accelerated Video Analysis
***********************************
.. toctree::
:maxdepth: 1
video
modules/gpuvideo/include/opencv2/gpuvideo.hpp 0 → 100644
浏览文件 @ fc1fa285
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_GPUVIDEO_HPP__
#define __OPENCV_GPUVIDEO_HPP__
#include <memory>
#include "opencv2/core/gpumat.hpp"
#include "opencv2/gpufilters.hpp"
namespace cv { namespace gpu {
////////////////////////////////// Optical Flow //////////////////////////////////////////
class CV_EXPORTS BroxOpticalFlow
{
public:
BroxOpticalFlow(float alpha_, float gamma_, float scale_factor_, int inner_iterations_, int outer_iterations_, int solver_iterations_) :
alpha(alpha_), gamma(gamma_), scale_factor(scale_factor_),
inner_iterations(inner_iterations_), outer_iterations(outer_iterations_), solver_iterations(solver_iterations_)
{
}
//! Compute optical flow
//! frame0 - source frame (supports only CV_32FC1 type)
//! frame1 - frame to track (with the same size and type as frame0)
//! u - flow horizontal component (along x axis)
//! v - flow vertical component (along y axis)
void operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& stream = Stream::Null());
//! flow smoothness
float alpha;
//! gradient constancy importance
float gamma;
//! pyramid scale factor
float scale_factor;
//! number of lagged non-linearity iterations (inner loop)
int inner_iterations;
//! number of warping iterations (number of pyramid levels)
int outer_iterations;
//! number of linear system solver iterations
int solver_iterations;
GpuMat buf;
};
class CV_EXPORTS PyrLKOpticalFlow
{
public:
PyrLKOpticalFlow();
void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
GpuMat& status, GpuMat* err = 0);
void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0);
void releaseMemory();
Size winSize;
int maxLevel;
int iters;
bool useInitialFlow;
private:
std::vector<GpuMat> prevPyr_;
std::vector<GpuMat> nextPyr_;
GpuMat buf_;
GpuMat uPyr_[2];
GpuMat vPyr_[2];
};
class CV_EXPORTS FarnebackOpticalFlow
{
public:
FarnebackOpticalFlow()
{
numLevels = 5;
pyrScale = 0.5;
fastPyramids = false;
winSize = 13;
numIters = 10;
polyN = 5;
polySigma = 1.1;
flags = 0;
}
int numLevels;
double pyrScale;
bool fastPyramids;
int winSize;
int numIters;
int polyN;
double polySigma;
int flags;
void operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null());
void releaseMemory()
{
frames_[0].release();
frames_[1].release();
pyrLevel_[0].release();
pyrLevel_[1].release();
M_.release();
bufM_.release();
R_[0].release();
R_[1].release();
blurredFrame_[0].release();
blurredFrame_[1].release();
pyramid0_.clear();
pyramid1_.clear();
}
private:
void prepareGaussian(
int n, double sigma, float *g, float *xg, float *xxg,
double &ig11, double &ig03, double &ig33, double &ig55);
void setPolynomialExpansionConsts(int n, double sigma);
void updateFlow_boxFilter(
const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat &flowy,
GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);
void updateFlow_gaussianBlur(
const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat& flowy,
GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);
GpuMat frames_[2];
GpuMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2];
std::vector<GpuMat> pyramid0_, pyramid1_;
};
// Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method
//
// see reference:
// [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
// [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
class CV_EXPORTS OpticalFlowDual_TVL1_GPU
{
public:
OpticalFlowDual_TVL1_GPU();
void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy);
void collectGarbage();
/**
* Time step of the numerical scheme.
*/
double tau;
/**
* Weight parameter for the data term, attachment parameter.
* This is the most relevant parameter, which determines the smoothness of the output.
* The smaller this parameter is, the smoother the solutions we obtain.
* It depends on the range of motions of the images, so its value should be adapted to each image sequence.
*/
double lambda;
/**
* Weight parameter for (u - v)^2, tightness parameter.
* It serves as a link between the attachment and the regularization terms.
* In theory, it should have a small value in order to maintain both parts in correspondence.
* The method is stable for a large range of values of this parameter.
*/
double theta;
/**
* Number of scales used to create the pyramid of images.
*/
int nscales;
/**
* Number of warpings per scale.
* Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale.
* This is a parameter that assures the stability of the method.
* It also affects the running time, so it is a compromise between speed and accuracy.
*/
int warps;
/**
* Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time.
* A small value will yield more accurate solutions at the expense of a slower convergence.
*/
double epsilon;
/**
* Stopping criterion iterations number used in the numerical scheme.
*/
int iterations;
double scaleStep;
bool useInitialFlow;
private:
void procOneScale(const GpuMat& I0, const GpuMat& I1, GpuMat& u1, GpuMat& u2);
std::vector<GpuMat> I0s;
std::vector<GpuMat> I1s;
std::vector<GpuMat> u1s;
std::vector<GpuMat> u2s;
GpuMat I1x_buf;
GpuMat I1y_buf;
GpuMat I1w_buf;
GpuMat I1wx_buf;
GpuMat I1wy_buf;
GpuMat grad_buf;
GpuMat rho_c_buf;
GpuMat p11_buf;
GpuMat p12_buf;
GpuMat p21_buf;
GpuMat p22_buf;
GpuMat diff_buf;
GpuMat norm_buf;
};
//! Calculates optical flow for 2 images using block matching algorithm */
CV_EXPORTS void calcOpticalFlowBM(const GpuMat& prev, const GpuMat& curr,
Size block_size, Size shift_size, Size max_range, bool use_previous,
GpuMat& velx, GpuMat& vely, GpuMat& buf,
Stream& stream = Stream::Null());
class CV_EXPORTS FastOpticalFlowBM
{
public:
void operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy, int search_window = 21, int block_window = 7, Stream& s = Stream::Null());
private:
GpuMat buffer;
GpuMat extended_I0;
GpuMat extended_I1;
};
//! Interpolate frames (images) using provided optical flow (displacement field).
//! frame0 - frame 0 (32-bit floating point images, single channel)
//! frame1 - frame 1 (the same type and size)
//! fu - forward horizontal displacement
//! fv - forward vertical displacement
//! bu - backward horizontal displacement
//! bv - backward vertical displacement
//! pos - new frame position
//! newFrame - new frame
//! buf - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 GpuMat;
//! occlusion masks 0, occlusion masks 1,
//! interpolated forward flow 0, interpolated forward flow 1,
//! interpolated backward flow 0, interpolated backward flow 1
//!
CV_EXPORTS void interpolateFrames(const GpuMat& frame0, const GpuMat& frame1,
const GpuMat& fu, const GpuMat& fv,
const GpuMat& bu, const GpuMat& bv,
float pos, GpuMat& newFrame, GpuMat& buf,
Stream& stream = Stream::Null());
CV_EXPORTS void createOpticalFlowNeedleMap(const GpuMat& u, const GpuMat& v, GpuMat& vertex, GpuMat& colors);
//////////////////////// Background/foreground segmentation ////////////////////////
// Foreground Object Detection from Videos Containing Complex Background.
// Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian.
// ACM MM2003 9p
class CV_EXPORTS FGDStatModel
{
public:
struct CV_EXPORTS Params
{
int Lc; // Quantized levels per 'color' component. Power of two, typically 32, 64 or 128.
int N1c; // Number of color vectors used to model normal background color variation at a given pixel.
int N2c; // Number of color vectors retained at given pixel. Must be > N1c, typically ~ 5/3 of N1c.
// Used to allow the first N1c vectors to adapt over time to changing background.
int Lcc; // Quantized levels per 'color co-occurrence' component. Power of two, typically 16, 32 or 64.
int N1cc; // Number of color co-occurrence vectors used to model normal background color variation at a given pixel.
int N2cc; // Number of color co-occurrence vectors retained at given pixel. Must be > N1cc, typically ~ 5/3 of N1cc.
// Used to allow the first N1cc vectors to adapt over time to changing background.
bool is_obj_without_holes; // If TRUE we ignore holes within foreground blobs. Defaults to TRUE.
int perform_morphing; // Number of erode-dilate-erode foreground-blob cleanup iterations.
// These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1.
float alpha1; // How quickly we forget old background pixel values seen. Typically set to 0.1.
float alpha2; // "Controls speed of feature learning". Depends on T. Typical value circa 0.005.
float alpha3; // Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1.
float delta; // Affects color and color co-occurrence quantization, typically set to 2.
float T; // A percentage value which determines when new features can be recognized as new background. (Typically 0.9).
float minArea; // Discard foreground blobs whose bounding box is smaller than this threshold.
// default Params
Params();
};
// out_cn - channels count in output result (can be 3 or 4)
// 4-channels require more memory, but a bit faster
explicit FGDStatModel(int out_cn = 3);
explicit FGDStatModel(const cv::gpu::GpuMat& firstFrame, const Params& params = Params(), int out_cn = 3);
~FGDStatModel();
void create(const cv::gpu::GpuMat& firstFrame, const Params& params = Params());
void release();
int update(const cv::gpu::GpuMat& curFrame);
//8UC3 or 8UC4 reference background image
cv::gpu::GpuMat background;
//8UC1 foreground image
cv::gpu::GpuMat foreground;
std::vector< std::vector<cv::Point> > foreground_regions;
private:
FGDStatModel(const FGDStatModel&);
FGDStatModel& operator=(const FGDStatModel&);
class Impl;
std::auto_ptr<Impl> impl_;
};
/*!
Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm
The class implements the following algorithm:
"An improved adaptive background mixture model for real-time tracking with shadow detection"
P. KadewTraKuPong and R. Bowden,
Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
*/
class CV_EXPORTS MOG_GPU
{
public:
//! the default constructor
MOG_GPU(int nmixtures = -1);
//! re-initiaization method
void initialize(Size frameSize, int frameType);
//! the update operator
void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = 0.0f, Stream& stream = Stream::Null());
//! computes a background image which are the mean of all background gaussians
void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const;
//! releases all inner buffers
void release();
int history;
float varThreshold;
float backgroundRatio;
float noiseSigma;
private:
int nmixtures_;
Size frameSize_;
int frameType_;
int nframes_;
GpuMat weight_;
GpuMat sortKey_;
GpuMat mean_;
GpuMat var_;
};
/*!
The class implements the following algorithm:
"Improved adaptive Gausian mixture model for background subtraction"
Z.Zivkovic
International Conference Pattern Recognition, UK, August, 2004.
http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
*/
class CV_EXPORTS MOG2_GPU
{
public:
//! the default constructor
MOG2_GPU(int nmixtures = -1);
//! re-initiaization method
void initialize(Size frameSize, int frameType);
//! the update operator
void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());
//! computes a background image which are the mean of all background gaussians
void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const;
//! releases all inner buffers
void release();
// parameters
// you should call initialize after parameters changes
int history;
//! here it is the maximum allowed number of mixture components.
//! Actual number is determined dynamically per pixel
float varThreshold;
// threshold on the squared Mahalanobis distance to decide if it is well described
// by the background model or not. Related to Cthr from the paper.
// This does not influence the update of the background. A typical value could be 4 sigma
// and that is varThreshold=4*4=16; Corresponds to Tb in the paper.
/////////////////////////
// less important parameters - things you might change but be carefull
////////////////////////
float backgroundRatio;
// corresponds to fTB=1-cf from the paper
// TB - threshold when the component becomes significant enough to be included into
// the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
// For alpha=0.001 it means that the mode should exist for approximately 105 frames before
// it is considered foreground
// float noiseSigma;
float varThresholdGen;
//correspondts to Tg - threshold on the squared Mahalan. dist. to decide
//when a sample is close to the existing components. If it is not close
//to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
//Smaller Tg leads to more generated components and higher Tg might make
//lead to small number of components but they can grow too large
float fVarInit;
float fVarMin;
float fVarMax;
//initial variance for the newly generated components.
//It will will influence the speed of adaptation. A good guess should be made.
//A simple way is to estimate the typical standard deviation from the images.
//I used here 10 as a reasonable value
// min and max can be used to further control the variance
float fCT; //CT - complexity reduction prior
//this is related to the number of samples needed to accept that a component
//actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
//the standard Stauffer&Grimson algorithm (maybe not exact but very similar)
//shadow detection parameters
bool bShadowDetection; //default 1 - do shadow detection
unsigned char nShadowDetection; //do shadow detection - insert this value as the detection result - 127 default value
float fTau;
// Tau - shadow threshold. The shadow is detected if the pixel is darker
//version of the background. Tau is a threshold on how much darker the shadow can be.
//Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
//See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
private:
int nmixtures_;
Size frameSize_;
int frameType_;
int nframes_;
GpuMat weight_;
GpuMat variance_;
GpuMat mean_;
GpuMat bgmodelUsedModes_; //keep track of number of modes per pixel
};
/**
* Background Subtractor module. Takes a series of images and returns a sequence of mask (8UC1)
* images of the same size, where 255 indicates Foreground and 0 represents Background.
* This class implements an algorithm described in "Visual Tracking of Human Visitors under
* Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere,
* A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012.
*/
class CV_EXPORTS GMG_GPU
{
public:
GMG_GPU();
/**
* Validate parameters and set up data structures for appropriate frame size.
* @param frameSize Input frame size
* @param min Minimum value taken on by pixels in image sequence. Usually 0
* @param max Maximum value taken on by pixels in image sequence. e.g. 1.0 or 255
*/
void initialize(Size frameSize, float min = 0.0f, float max = 255.0f);
/**
* Performs single-frame background subtraction and builds up a statistical background image
* model.
* @param frame Input frame
* @param fgmask Output mask image representing foreground and background pixels
* @param stream Stream for the asynchronous version
*/
void operator ()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());
//! Releases all inner buffers
void release();
//! Total number of distinct colors to maintain in histogram.
int maxFeatures;
//! Set between 0.0 and 1.0, determines how quickly features are "forgotten" from histograms.
float learningRate;
//! Number of frames of video to use to initialize histograms.
int numInitializationFrames;
//! Number of discrete levels in each channel to be used in histograms.
int quantizationLevels;
//! Prior probability that any given pixel is a background pixel. A sensitivity parameter.
float backgroundPrior;
//! Value above which pixel is determined to be FG.
float decisionThreshold;
//! Smoothing radius, in pixels, for cleaning up FG image.
int smoothingRadius;
//! Perform background model update.
bool updateBackgroundModel;
private:
float maxVal_, minVal_;
Size frameSize_;
int frameNum_;
GpuMat nfeatures_;
GpuMat colors_;
GpuMat weights_;
Ptr<FilterEngine_GPU> boxFilter_;
GpuMat buf_;
};
}} // namespace cv { namespace gpu {
#endif /* __OPENCV_GPUVIDEO_HPP__ */
modules/gpuvideo/perf/perf_main.cpp 0 → 100644
浏览文件 @ fc1fa285
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "perf_precomp.hpp"
using namespace perf;
CV_PERF_TEST_MAIN(gpuarithm, printCudaInfo())
modules/gpuvideo/perf/perf_precomp.cpp 0 → 100644
浏览文件 @ fc1fa285
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "perf_precomp.hpp"
modules/gpuvideo/perf/perf_precomp.hpp 0 → 100644
浏览文件 @ fc1fa285
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifdef __GNUC__
# pragma GCC diagnostic ignored "-Wmissing-declarations"
# if defined __clang__ || defined __APPLE__
# pragma GCC diagnostic ignored "-Wmissing-prototypes"
# pragma GCC diagnostic ignored "-Wextra"
# endif
#endif
#ifndef __OPENCV_PERF_PRECOMP_HPP__
#define __OPENCV_PERF_PRECOMP_HPP__
#include "opencv2/ts.hpp"
#include "opencv2/ts/gpu_perf.hpp"
#include "opencv2/gpuvideo.hpp"
#include "opencv2/gpuimgproc.hpp"
#include "opencv2/video.hpp"
#include "opencv2/legacy.hpp"
#ifdef GTEST_CREATE_SHARED_LIBRARY
#error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined
#endif
#endif
modules/gpunvidia/src/cuda/NCVBroxOpticalFlow.cu → modules/gpuvideo/src/cuda/NCVBroxOpticalFlow.cu
浏览文件 @ fc1fa285
......@@ -64,7 +64,7 @@
#include "opencv2/core/cuda/utility.hpp"
#include "opencv2/gpunvidia/NPP_staging.hpp"
#include "opencv2/gpunvidia/NCVBroxOpticalFlow.hpp"
#include "opencv2/gpuvideo/NCVBroxOpticalFlow.hpp"
typedef NCVVectorAlloc<Ncv32f> FloatVector;
......
modules/gpuvideo/src/precomp.cpp 0 → 100644
浏览文件 @ fc1fa285
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
modules/gpuvideo/src/precomp.hpp 0 → 100644
浏览文件 @ fc1fa285
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#include <limits>
#include "opencv2/gpuvideo.hpp"
#include "opencv2/gpuarithm.hpp"
#include "opencv2/gpufilters.hpp"
#include "opencv2/gpuimgproc.hpp"
#include "opencv2/video.hpp"
#include "opencv2/core/gpu_private.hpp"
#include "opencv2/opencv_modules.hpp"
#ifdef HAVE_OPENCV_GPUNVIDIA
# include "opencv2/gpunvidia/private.hpp"
# include "opencv2/gpuvideo/NCVBroxOpticalFlow.hpp"
#endif
#ifdef HAVE_CUDA
# include "cuda/fgd_bgfg_common.hpp"
#endif
#endif /* __OPENCV_PRECOMP_H__ */
modules/gpuvideo/test/test_main.cpp 0 → 100644
浏览文件 @ fc1fa285
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
CV_GPU_TEST_MAIN("gpu")
modules/gpuvideo/test/test_precomp.cpp 0 → 100644
浏览文件 @ fc1fa285
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
modules/gpuvideo/test/test_precomp.hpp 0 → 100644
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifdef __GNUC__
# pragma GCC diagnostic ignored "-Wmissing-declarations"
# if defined __clang__ || defined __APPLE__
# pragma GCC diagnostic ignored "-Wmissing-prototypes"
# pragma GCC diagnostic ignored "-Wextra"
# endif
#endif
#ifndef __OPENCV_TEST_PRECOMP_HPP__
#define __OPENCV_TEST_PRECOMP_HPP__
#include <fstream>
#include "opencv2/ts.hpp"
#include "opencv2/ts/gpu_test.hpp"
#include "opencv2/gpuvideo.hpp"
#include "opencv2/gpuimgproc.hpp"
#include "opencv2/video.hpp"
#include "opencv2/legacy.hpp"
#endif
samples/cpp/CMakeLists.txt
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......@@ -21,6 +21,7 @@ if(BUILD_EXAMPLES AND OCV_DEPENDENCIES_FOUND)
ocv_include_directories("${OpenCV_SOURCE_DIR}/modules/gpufilters/include")
ocv_include_directories("${OpenCV_SOURCE_DIR}/modules/gpuimgproc/include")
ocv_include_directories("${OpenCV_SOURCE_DIR}/modules/gpufeatures2d/include")
ocv_include_directories("${OpenCV_SOURCE_DIR}/modules/gpuvideo/include")
ocv_include_directories("${OpenCV_SOURCE_DIR}/modules/gpu/include")
endif()
......
samples/gpu/CMakeLists.txt
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......@@ -2,7 +2,7 @@ SET(OPENCV_GPU_SAMPLES_REQUIRED_DEPS opencv_core opencv_flann opencv_imgproc ope
opencv_ml opencv_video opencv_objdetect opencv_features2d
opencv_calib3d opencv_legacy opencv_contrib opencv_gpu
opencv_nonfree opencv_softcascade opencv_superres
opencv_gpucodec opencv_gpuarithm opencv_gpufilters opencv_gpunvidia opencv_gpuimgproc opencv_gpufeatures2d)
opencv_gpucodec opencv_gpuarithm opencv_gpufilters opencv_gpunvidia opencv_gpuimgproc opencv_gpufeatures2d opencv_gpuvideo)
ocv_check_dependencies(${OPENCV_GPU_SAMPLES_REQUIRED_DEPS})
......
samples/gpu/opticalflow_nvidia_api.cpp
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......@@ -17,6 +17,7 @@
#ifdef HAVE_CUDA
#include "opencv2/gpunvidia.hpp"
#include "opencv2/gpuvideo/NCVBroxOpticalFlow.hpp"
#endif
#if !defined(HAVE_CUDA)
......
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